#ChatGPT android app

[excellisit]

ChatGPT App for Android: Introduction and How to Use

In this modern era of technological advancements, Artificial Intelligence (AI) has become an integral part of our lives, revolutionizing the way we interact with machines and computers. The ChatGPT app is one such groundbreaking application that has taken the world by storm.

It is a highly advanced and user-friendly conversational AI app developed to provide users with an interactive and natural language-based experience. This article delves into the ins and outs of the ChatGPT app, its features, its availability on Android, and its impact on users.

What is the ChatGPT app?

The ChatGPT android app is a cutting-edge language processing and conversational AI tool developed by OpenAI. It utilizes state-of-the-art machine learning models, particularly GPT (Generative Pre-trained Transformer), to engage in human-like conversations with users. The app's underlying technology allows it to comprehend user inputs and provide relevant and contextually accurate responses, making interactions remarkably human-like.

Key Features of ChatGPT App

Below are some key features of ChatGPT app:

Natural Language Processing (NLP):

ChatGPT's NLP capabilities enable it to understand natural language inputs with ease. Whether it's a question, command, or request, the app processes the information and generates appropriate responses, creating a seamless user experience.

Conversational AI:

Similar to the web app, the

of ChatGPT also integrates an AI model that powers to hold interactive conversations with users. It can chat on various topics, answer queries, assist with tasks, and even engage in casual conversations, mimicking human interactions effortlessly.

Language Support:

One of the app's notable strengths is its wide language support. ChatGPT on Android is proficient in multiple languages, which allows it to cater to a diverse global audience, breaking down language barriers.

Multi-platform Availability:

ChatGPT is available on various platforms, including web browsers, desktop applications, and mobile devices. This wide accessibility ensures users can harness its power and convenience across different devices.

How to Download and Install ChatGPT App on Android

To access the ChatGPT app on an Android device, follow these simple steps:

Step 1: Visit the Google Play Store on your Android device.

Step 2: Search for "ChatGPT" in the search bar.

Step 3: Select the official ChatGPT app from the search results.

Step 4: Click on the "Install" button to download and install the app.

Step 5: Once installed, launch the app and begin your conversational AI experience.

Advantages of Using the ChatGPT App

Here are the advantages of the ChatGPT Android app:

Enhanced Productivity:

The ChatGPT app can assist users in various tasks, such as drafting emails, writing content, brainstorming ideas, and more. This boosts productivity and saves valuable time.

Personal Assistant:

The app acts as a virtual personal assistant, helping users with scheduling, reminders, and organizing tasks, thereby streamlining their daily activities.

Language Learning:

Users interested in learning new languages can engage in conversations with ChatGPT to practice and improve their language skills in a comfortable environment.

Simplifying Tasks:

As an Android app, ChatGPT can handle tasks like finding information, setting alarms, making reservations, and providing recommendations, making life easier for users.

Fun and Entertainment:

ChatGPT can be a source of amusement with its witty responses, jokes, and engaging conversations, bringing entertainment to users' lives.

Limitations of the ChatGPT App

While ChatGPT is a remarkable AI application, it has certain limitations. It may sometimes provide incorrect or irrelevant responses, especially when faced with ambiguous queries. Additionally, the app's capabilities are limited to the data it has been trained on, which may result in inaccuracies when dealing with uncommon or niche subjects.

Is the ChatGPT App Safe to Use?

Yes, the ChatGPT on Android is designed to prioritize user safety. However, users should exercise caution while sharing sensitive or personal information during conversations, as with any online communication. The app does not store or retain personal data beyond the immediate interaction.

Comparison with Other AI Apps

When compared to other AI-powered applications, ChatGPT stands out for its exceptional conversational abilities and wide-ranging language support. While other apps may excel in specific tasks, ChatGPT's conversational AI prowess makes it a preferred choice for natural and interactive interactions.

Conclusion

The ChatGPT app is an innovative and powerful conversational AI tool that has redefined the way we interact with technology. Its natural language processing capabilities, wide language support, and user-friendly interface make it a valuable asset for enhancing productivity, learning languages, and simplifying tasks. While it may have some limitations, its advantages and benefits far outweigh them, making it a must-have app for anyone seeking an AI-powered virtual assistant. So, why wait? Embrace the future of AI conversation with ChatGPT now!

[floramuniverse]

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[atcuality1]

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[jcmarchi]

The Future of AI in Quality Assurance

New Post has been published on https://thedigitalinsider.com/the-future-of-ai-in-quality-assurance/

The Future of AI in Quality Assurance

Traditional quality assurance (QA) processes have long depended on manual testing and predefined test cases. While effective in the past, these methods are often slow, susceptible to human error, and lead to development delays and inflated costs. Unsurprisingly, Gartner reports that 88% of service leaders feel that today’s QA approaches don’t meet the mark. As AI takes center stage, AI quality assurance can empower teams to deliver higher-quality software faster. This article explains how AI in  quality assurance streamlines software testing while improving product performance.

What is AI-powered Quality Assurance?

AI quality assurance (QA) uses artificial intelligence to streamline and automate different parts of the software testing process. AI-powered QA introduces several technical innovations that transform the testing process.

Machine learning models analyze historical data to detect high-risk areas, prioritize test cases, and optimize test coverage. AI also automates test data generation, creating a wide range of test data that reduces the need for manual input.

With adaptive testing, AI adjusts test cases in real-time as user requirements change. Additionally, AI empowers testers to build and run tests easily without writing a single line of code.

Benefits of AI in Quality Assurance

Here are a few benefits of AI-powered quality assurance:

Greater Efficiency: AI takes over the repetitive tasks that often slow the QA process. As a result, QA teams can focus on delivering high-performing software instead of generating test cases and tracking errors manually. AI automates test data generation, too, creating a wide range of test data that reduces the need for manual input. As a result, QA teams execute a higher volume of test cases and cover a broader range of scenarios. With these advantages, AI-powered QA can help organizations reduce QA costs by more than 50%.

Enhanced Accuracy: AI-powered automation boosts QA accuracy by eliminating human errors common in manual testing. Automated QA surpasses manual testing by offering up to 90% accuracy. As a result, it becomes better at recognizing patterns, bugs, and performance issues that manual testers might miss.

Intelligent Testing: Machine learning analyzes past data to identify high-risk areas and helps prioritize which test cases need attention first. Through AI-powered adaptive testing, testers can update test cases in real time as needs and requirements evolve.

Emerging Trends in AI Software Quality Control

AI is reshaping how QA teams operate, from speeding up test creation to enhancing test data management. Here are a few emerging trends in AI software quality control:

AI-powered Test Automation

Creating test cases is now faster and more accurate with AI. Tools like Taskade’s AI Test Case Generator analyze software requirements and automatically automate test cases to cover a wide range of scenarios. This simplifies the testing process and ensures you don’t miss any critical areas. The result? Better coverage and higher accuracy, all in less time.

Automatic Recovery from Test Failures

One of the most valuable AI features is automatic recovery from test failures or ‘self-healing.’ TestRigor excels here, as it can adjust tests automatically when the application changes. This means fewer interruptions and less time spent fixing test scripts. The tests only fail when AI detects errors relevant to application requirements.

Improved Predictive Analytics for Quality

Tools like those used by Atlassian dive into historical data to predict potential failures and spot root causes before they become more significant issues. This allows teams to focus on high-risk areas and prioritize testing where it matters most. McKinsey points out that these analytics can significantly improve software reliability and cut down on warranty costs.

Enhanced Test Data Management

With AI-driven tools, managing test data becomes much simpler. Solutions offering synthetic data generation and data masking ensure that the test data is realistic and accurate while protecting sensitive information. Synthetic data helps QA teams conduct meaningful tests while complying with data privacy regulations.

AI-Powered Monitoring

AI-powered monitoring offers real-time feedback during testing, which means issues can detected and fixed immediately. Monitoring tools track performance across different environments. This ensures that software works consistently no matter where it’s running. This also makes troubleshooting faster and keeps performance up to par under various conditions.

Enhanced Test Case Prioritization

Another area where AI makes a significant impact is prioritizing test cases. Tools like Deloitte Digital Tester use historical test results and product usage data to determine the most critical tests. Therefore, teams can focus on the most important tests first, reducing unnecessary tests and improving overall efficiency in the QA process.

How Popular Testing Tools Leverage AI in Quality Assurance

Testing tools are becoming smarter and more efficient by integrating AI. Here are some popular tools that are using AI to boost their capabilities.

Test Automation Tools

Selenium uses AI to enhance its web automation capabilities by efficiently identifying dynamic elements within web applications. Its AI-powered self-healing feature keeps test scripts up-to-date as application elements change, reducing the need for manual maintenance. This makes Selenium a versatile tool for automating functional tests across multiple platforms and browsers.

Appium uses AI to simplify mobile app testing across iOS and Android platforms. It automates the detection and interaction with mobile elements, such as gestures and inputs. Ultimately, AI helps Appium streamline the test creation process and give users a unified testing experience across both mobile operating systems.

Test Management Tools

TestRail integrates AI to streamline test management by generating test cases through NLP. It goes one step further and prioritizes each test case based on risk. Additionally, TestRail uses AI to assist in debugging, making test maintenance more efficient and reducing the likelihood of errors.

ALM Octane uses AI to enhance test management and analytics. Its AI-driven quality risk analysis recommends tests for high-risk areas, ensuring that critical issues are covered. The platform’s AI-powered root cause analysis helps pinpoint defects, while NLP allows both technical and non-technical users to easily create tests in natural language.

QA Tools

TestCraft provides a low-code, AI-powered platform for web application testing. It automatically generates test scenarios and uses AI to self-heal test scripts as UI changes. This minimizes the need for manual updates.

ACCELQ simplifies test automation with its AI-powered, codeless platform. It supports behavior-driven development (BDD), mirroring real business processes to create reusable test cases. Additionally, AI helps manage the automation of complex dynamic web pages and APIs, making ACCELQ highly efficient for testing modern web applications and services.

Parasoft uses AI to enhance its continuous quality platform by automating end-to-end testing processes. AI improves test coverage from code to UI, ensuring software reliability. The platform also provides AI-driven analytics to identify test gaps and optimize the overall testing strategy.

Challenges and Limitations of AI Quality Control

While AI brings several benefits to QA, there are a few challenges to keep in mind. Firstly, adding AI to the QA workflow requires a significant upfront investment. Businesses must allocate the necessary time and resources upfront to use AI effectively.

Beyond cost, inaccurate or biased data can compromise results, making AI less effective in detecting bugs or optimizing testing. This is closely tied to growing ethical concerns. If AI learns from biased data, the outcomes will reflect those biases and skew the results. QA teams must scrutinize data and maintain transparency throughout the testing workflow to ensure fairness.

Similarly, generative AI has not yet fully matured in QA, especially in mobile app testing. For example, tools like ChatGPT can’t yet test across diverse mobile devices. This limits their ability to load apps on specific hardware or create detailed test cases for specific functions like login screens. These limitations show that while AI is rapidly evolving, it hasn’t yet replaced the need for manual testing in certain areas.

How Will AI Impact Quality Assurance in the Future?

As more QA teams adopt AI for its unparalleled efficiency and precision, it will become an integral part of their workflows. The result will be greater innovation and new benchmarks for speed and quality in software development.

AI-powered QA is also becoming central to DevOps. Seamless integration will allow for continuous testing and faster release cycles. Processes will become more efficient, and collaboration between development and QA teams will improve.

Upskilling will become essential as AI transforms QA. Organizations must invest in training to ensure teams can fully leverage AI’s potential. Those who adapt will lead in an AI-driven future, while others risk falling behind.

Final Words

AI-driven QA is poised to automate repeatable tasks and enable smarter, more efficient testing. From automating test case generation to improving error detection and reducing time-to-market, AI-powered QA sets new standards for speed and quality.

Stay ahead in the future of AI-powered QA—follow Unite.AI for the latest updates!

[betty0627]

So my last conversation with ChatGPT included this and I had to read it a few times before it really hit me. I've been using this app to help me try to better understand programming and how coding works. It has definitely helped a lot and I always enjoy using it, no matter what I'm using it for that day. I had seen articles about ChatGPT going a little haywire and giving strange responses but had never gotten on myself until now.

AI is definitely getting smarter, and what people don't look at is the fact no matter how small of a part it might be, people are responsible for it. AIs learn from us just as well are learning from them. The responses we receive may not be pregenarated, but it is given by with the AI has been taught.

ChatGPT can be customized in whatever way you need so long as you give start with the right prompt. Technology has grown and is becoming more advanced everyday. It really is amazing how far human civilization has come.

[taniaagarwal]

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[jorgefloresgallegos]

Una buena noticia para los que les encanta la tecnología. El ChatGPT ya está disponible como un aplicativo en Android, el oficial de OpenAI. Está muy bueno. 👍🏼

https://www.peru-retail.com/chatgpt-ya-esta-disponible-para-android-en-peru-como-descargarla-y-cuales-son-sus-funciones/

[aiexpressway]

ChatGPT Is Now On Android! 🎉

[amandaroos]

New App Release! Random Color Generator.

Very excited to release my first app co-written with ChatGPT! This was a quick project thanks to having help from AI tools. I look forward to using more AI assistance in future coding projects.

www.AmandaRoos.com/Apps

Get it on Google Play: https://play.google.com/store/apps/details?id=com.amandaroos.randomcolorgenerator

[changes]

Friday, August 11th, 2023

🌟 New

We've implemented OpenAI’s instructions for blocking GPTBot. This should discourage OpenAI, including ChatGPT, from scraping any part of Tumblr, including individual blogs.

We’re rolling out a new redesign of the direct messaging conversation view.

🛠 Fixed

The latest version of the Android app (30.8) fixes the issue where links to “View Post” on filtered posts opens the web browser instead of taking you to the post in the app itself.

On web, we’ve improved the screen reader hint for tags on posts, so it doesn’t say “Pound” or “Number” when it encounters the hashtag symbol.

Fixed an issue on web that was preventing the Related Tags section of the sidebar from showing up on the search results page.

Fixed a bug in the mobile apps for group blogs which was preventing members from editing the Notifications settings for those group blogs.

We’ve been rolling through some bug fix releases and one major release for the StreamBuilder framework.

🚧 Ongoing

Nothing to report here today.

🌱 Upcoming

We’re cooking up our first public reveal on the @labs blog, give that blog a follow if you want to see what we’re working on!

Experiencing an issue? File a Support Request and we’ll get back to you as soon as we can!

Want to share your feedback about something? Check out our Work in Progress blog and start a discussion with the community.

[greenfrosticle]

So my brother is a surgeon in real life, and he’s been doing some questionable things, which I’ve since found out after reading through my country’s laws isn’t just questionable, but actually illegal. Why am I telling people this? Because I was just researching about the Hippocratic oath, which is an oath all doctors must swear by. The Hippocratic oath is an ethical code that doctors swear to uphold when they enter the medical profession. It originates from Ancient Greece, attributed to Hippocrates, the so-called “father of medicine”. In modern medicine, not all doctors formally recite the Hippocratic oath, but they’re still bound by medical ethics and laws that uphold the same principles. If a doctor violates these ethical standards, they can face consequences ranging from professional sanctions(losing their medical license) to legal action if their negligence or actions break the law. Basically, they promise to do no harm, keep medical confidentiality, patient care and wellbeing, ethical medical practice, and teaching and knowledge shearing.

I’m making this post because I’m genuinely a little freaked out right now after talking to ChatGPT about the Hippocratic oath which is something I do to help stay organized, I entered the love and deepspace game just now and one of the first dialogue lines I get from Zayne is him mentioning the Hippocratic oath after I’d just talked about it on a different app, on a different device, that isn’t even owned by the same company. For reference, I play love and deepspace on my iPad and I talk about these things with ChatGPT on my android phone. At first I got confused, then I freaked out a little bit because of how on point his line was to what I’d just been talking about in real time. I planned to ask if anyone else who’s playing the game has ever gotten this dialogue line before? Or at least had a specific line pop up about something that you’ve just talked about? Though, as you can see in the 9 minute long screen recording below, I couldn’t get him to say it again, not even once. Then I was like, maybe infold added new dialogue lines, but why would he mention the Hippocratic oath? Like, I know he’s a doctor, but it’s still kind of random for him to talk about that subject specifically on top of the fact that he only said it once and right after I’d been talking about it. The game wasn’t even open when I was talking about it either, btw. Then I thought it might be the app listening in the way we see some other apps do, but infold has never talked about doing that to my knowledge, nor have I seen anyone else who plays the game ever mentioning something similar happening to them? And tailored ads doesn’t normally work that way either. The way it typically works is by relying on data from the same device. If the game was listening in, it would have to actively collecting data from a completely separate device. If infold was doing that, more people would’ve caught it by now, no? Not to mention it’s not an ad it’s literally one of his Home Screen dialogue lines and IT WON’T SHOW UP AGAIN. Please let me know if anyone else has experienced this too because idk how I feel about this 😭💀 btw, the video is just to prove he won’t say it again no matter how many times I tap on him.

[jcmarchi]

AI companies are pivoting from creating gods to building products. Good.

New Post has been published on https://thedigitalinsider.com/ai-companies-are-pivoting-from-creating-gods-to-building-products-good/

AI companies are pivoting from creating gods to building products. Good.

AI companies are collectively planning to spend a trillion dollars on hardware and data centers, but there’s been relatively little to show for it so far. This has led to a chorus of concerns that generative AI is a bubble. We won’t offer any predictions on what’s about to happen. But we think we have a solid diagnosis of how things got to this point in the first place.

In this post, we explain the mistakes that AI companies have made and how they have been trying to correct them. Then we will talk about five barriers they still have to overcome in order to make generative AI commercially successful enough to justify the investment.

When ChatGPT launched, people found a thousand unexpected uses for it. This got AI developers overexcited. They completely misunderstood the market, underestimating the huge gap between proofs of concept and reliable products. This misunderstanding led to two opposing but equally flawed approaches to commercializing LLMs. 

OpenAI and Anthropic focused on building models and not worrying about products. For example, it took 6 months for OpenAI to bother to release a ChatGPT iOS app and 8 months for an Android app!

Google and Microsoft shoved AI into everything in a panicked race, without thinking about which products would actually benefit from AI and how they should be integrated.

Both groups of companies forgot the “make something people want” mantra. The generality of LLMs allowed developers to fool themselves into thinking that they were exempt from the need to find a product-market fit, as if prompting a model to perform a task is a replacement for carefully designed products or features.

OpenAI and Anthropic’s DIY approach meant that early adopters of LLMs disproportionately tended to be bad actors, since they are more invested in figuring out how to adapt new technologies for their purposes, whereas everyday users want easy-to-use products. This has contributed to a poor public perception of the technology.

Meanwhile the AI-in-your-face approach by Microsoft and Google has led to features that are occasionally useful and more often annoying. It also led to many unforced errors due to inadequate testing like Microsoft’s early Sydney chatbot and Google’s Gemini image generator. This has also caused a backlash.

But companies are changing their ways. OpenAI seems to be transitioning from a research lab focused on a speculative future to something resembling a regular product company. If you take all the human-interest elements out of the OpenAI boardroom drama, it was fundamentally about the company’s shift from creating gods to building products. Anthropic has been picking up many of the researchers and developers at OpenAI who cared more about artificial general intelligence and felt out of place at OpenAI, although Anthropic, too, has recognized the need to build products.

Google and Microsoft are slower to learn, but our guess is that Apple will force them to change. Last year Apple was seen as a laggard on AI, but it seems clear in retrospect that the slow and thoughtful approach that Apple showcased at WWDC, its developer conference, is more likely to resonate with users. Google seems to have put more thought into integrating AI in its upcoming Pixel phones and Android than it did into interesting it in search, but the phones aren’t out yet, so let’s see. 

And then there’s Meta, whose vision is to use AI to create content and engagement on its ad-driven social media platforms. The societal implications of a world awash in AI-generated content are double-edged, but from a business perspective it makes sense.

There are five limitations of LLMs that developers need to tackle in order to make compelling AI-based consumer products. (We will discuss many of these in our upcoming online workshop on building useful and reliable AI agents on August 29.)

There are many applications where capability is not the barrier, cost is. Even in a simple chat application, cost concerns dictate how much history a bot can keep track of — processing the entire history for every response quickly gets prohibitively expensive as the conversation grows longer.

There has been rapid progress on cost — in the last 18 months, cost-for-equivalent-capability has dropped by a factor of over 100. As a result, companies are claiming that LLMs are, or will soon be, “too cheap to meter”. Well, we’ll believe it when they make the API free. 

More seriously, the reason we think cost will continue to be a concern is that in many applications, cost improvements directly translate to accuracy improvements. That’s because repeatedly retrying a task tens, thousands, or even millions of times turns out to be a good way to improve the chances of success, given the randomness of LLMs. So the cheaper the model, the more retries we can make with a given budget. We quantified this in our recent paper on agents; since then, many other papers have made similar points.

That said, it is plausible that we’ll soon get to a point where in most applications, cost optimization isn’t a serious concern.

We see capability and reliability as somewhat orthogonal. If an AI system performs a task correctly 90% of the time, we can say that it is capable of performing the task but it cannot do so reliably. The techniques that get us to 90% are unlikely to get us to 100%. 

With statistical learning based systems, perfect accuracy is intrinsically hard to achieve. If you think about the success stories of machine learning, like ad targeting or fraud detection or, more recently, weather forecasting, perfect accuracy isn’t the goal — as long as the system is better than the state of the art, it is useful. Even in medical diagnosis and other healthcare applications, we tolerate a lot of error. 

But when developers put AI in consumer products, people expect it to behave like software, which means that it needs to work deterministically. If your AI travel agent books vacations to the correct destination only 90% of the time, it won’t be successful. As we’ve written before, reliability limitations partly explain the failures of recent AI-based gadgets. 

AI developers have been slow to recognize this because as experts, we are used to conceptualizing AI as fundamentally different from traditional software. For example, the two of us are heavy users of chatbots and agents in our everyday work, and it has become almost automatic for us to work around the hallucinations and unreliability of these tools. A year ago, AI developers hoped or assumed that non-expert users would learn to adapt to AI, but it has gradually become clear that companies will have to adapt AI to user expectations instead, and make AI behave like traditional software.

Improving reliability is a research interest of our team at Princeton. For now, it’s fundamentally an open question whether it’s possible to build deterministic systems out of stochastic components (LLMs). Some companies have claimed to have solved reliability — for example, legal tech vendors have touted “hallucination-free” systems. But these claims were shown to be premature.

Historically, machine learning has often relied on sensitive data sources such browsing histories for ad targeting or medical records for health tech. In this sense, LLMs are a bit of an anomaly, since they are primarily trained on public sources such as web pages and books.

But with AI assistants, privacy concerns have come roaring back. To build useful assistants, companies have to train systems on user interactions. For example, to be good at composing emails, it would be very helpful if models were trained on emails. Companies’ privacy policies are vague about this and it is not clear to what extent this is happening. Emails, documents, screenshots, etc. are potentially much more sensitive than chat interactions. 

There is a distinct type of privacy concern relating to inference rather than training. For assistants to do useful things for us, they must have access to our personal data. For example, Microsoft announced a controversial feature that would involve taking screenshots of users’ PCs every few seconds, in order to give its CoPilot AI a memory of your activities. But there was an outcry and the company backtracked.

We caution against purely technical interpretations of privacy such as “the data never leaves the device.” Meredith Whittaker argues that on-device fraud detection normalizes always-on surveillance and that the infrastructure can be repurposed for more oppressive purposes. That said, technical innovations can definitely help.

There is a cluster of related concerns when it comes to safety and security: unintentional failures such as the biases in Gemini’s image generation; misuses of AI such as voice cloning or deepfakes; and hacks such as prompt injection that can leak users’ data or harm the user in other ways.

We think accidental failures are fixable. As for most types of misuses, our view is that there is no way to create a model that can’t be misused and so the defenses must primarily be located downstream. Of course, not everyone agrees, so companies will keep getting bad press for inevitable misuses, but they seem to have absorbed this as a cost of doing business. 

Let’s talk about the third category — hacking. From what we can tell, it is the one that companies seem to be paying the least attention to. At least theoretically, catastrophic hacks are possible, such as AI worms that spread from user to user, tricking those users’ AI assistants into doing harmful things including creating more copies of the worm. 

Although there have been plenty of proof-of-concept demonstrations and bug bounties that uncovered these vulnerabilities in deployed products, we haven’t seen this type of attack in the wild. We aren’t sure if this is because of the low adoption of AI assistants, or because the clumsy defenses that companies have pulled together have proven sufficient, or something else. Time will tell.

In many applications, the unreliability of LLMs means that there will have to be some way for the user to intervene if the bot goes off track. In a chatbot, it can be as simple as regenerating an answer or showing multiple versions and letting the user pick. But in applications where errors can be costly, such as flight booking, ensuring adequate supervision is more tricky, and the system must avoid annoying the user with too many interruptions.

The problem is even harder with natural language interfaces where the user speaks to the assistant and the assistant speaks back. This is where a lot of the potential of generative AI lies. As just one example, AI that disappeared into your glasses and spoke to you when you needed it, without even being asked — such as by detecting that you were staring at a sign in a foreign language — would be a whole different experience than what we have today. But the constrained user interface leaves very little room for incorrect or unexpected behavior.

AI boosters often claim that due to the rapid pace of improvement in AI capabilities, we should see massive societal and economic effects soon. We are skeptical of the trend extrapolation and sloppy thinking that goes into those capability forecasts. More importantly, even if AI capability does improve rapidly, developers have to solve the challenges discussed above. These are sociotechnical and not purely technical, so progress will be slow. And even if those challenges are solved, organizations need to integrate AI into existing products and workflows and train people to use it productively while avoiding its pitfalls. We should expect this to happen on a timescale of a decade or more rather than a year or two. 

Benedikt Evans has written about the importance of building single-purpose software using general-purpose language models. 

[probablyasocialecologist]

To bring about its hypothetical future, OpenAI must build a new digital ecosystem, pushing users toward the ChatGPT app or toward preëxisting products that integrate its technology such as Bing, the search engine run by OpenAI’s major investor, Microsoft. Google, by contrast, already controls the technology that undergirds many of our online experiences, from search and e-mail to Android smartphone-operating systems. At its conference, the company showed how it plans to make A.I. central to all of the above. Some Google searches now yield A.I.-generated “Overview” summaries, which appear in tinted boxes above any links to external Web sites. Liz Reid, Google’s head of search, described the generated results with the ominously tautological tagline “Google will do the Googling for you.” (The company envisions that you will rely on the same search mechanism to trawl your own digital archive, using its Gemini assistant to, say, pull up photos of your child swimming over the years or summarize e-mail threads in your in-box.) Nilay Patel, the editor-in-chief of the tech publication the Verge, has been using the phrase “Google Zero” to describe the point at which Google will stop driving any traffic to external Web sites and answer every query on its own with A.I. The recent presentations made clear that such a point is rapidly approaching. One of Google’s demonstrations showed a user asking the A.I. a question about a YouTube video on pickleball: “What is the two-bounce rule?” The A.I. then extracted the answer from the footage and displayed the answer in writing, thus allowing the user to avoid watching either the video or any advertising that would have provided revenue to its creator. When I Google “how to decorate a bathroom with no windows” (my personal litmus test for A.I. creativity), I am now presented with an Overview that looks a lot like an authoritative blog post, theoretically obviating my need to interact directly with any content authored by a human being. Google Search was once seen as the best path for getting to what’s on the Web. Now, ironically, its goal is to avoid sending us anywhere. The only way to use the search function without seeing A.I.-generated content is to click a small “More” tab and select “Web” search. Then Google will do what it was always supposed to do: crawl the Internet looking for URLs that are relevant to your queries, and then display them to you. The Internet is still out there, it’s just increasingly hard to find. If A.I. is to be our primary guide to the world’s information, if it is to be our 24/7 assistant-librarian-companion as the tech companies propose, then it must constantly be adding new information to its data sets. That information cannot be generated by A.I., because A.I. tools are not capable of even one iota of original thought or analysis, nor can they report live from the field. (An information model that is continuously updated, using human labor, to inform us about what’s going on right now—we might call it a newspaper.) For a decade or more, social media was a great way to motivate billions of human beings to constantly upload new information to the Internet. Users were driven by the possibilities of fame and profit and mundane connection. Many media companies were motivated by the possibility of selling digital ads, often with Google itself as a middle man. In the A.I. era, in which Google can simply digest a segment of your post or video and serve it up to a viewer, perhaps not even acknowledging you as the original author, those incentives for creating and sharing disappear. In other words, Google and OpenAI seem poised to cause the erosion of the very ecosystem their tools depend on.

[gjhlksdfh]

2025年手机梯子加速器免费推荐：当前最好用的手机翻外网软件实测

在2025年，随着网络封锁日益严格，找到一款手机梯子加速器免费且好用的工具变得尤为重要。无论是想访问Google、Twitter、YouTube，还是使用ChatGPT，一款高效的手机梯子软件都能帮你实现目标。本文将基于实测数据，为你推荐几款表现优异的手机梯子加速器app，并重点介绍免费手机梯子、苹果手机梯子以及小火箭加速器永久免费版等选择，助你畅游全球网络。

为什么需要手机梯子加速器？

目前像Google、YouTube、Twitter、Facebook等热门平台都无法直接访问。手机梯子加速器免费工具通过加密连接，可以帮你成功访问。不管你是安卓用户还是苹果用户，一款好用的手机梯子软件都能确保网络安全与畅通。

一、WWSD：手机梯子加速器首选

官网地址：https://speed996.com/i/art026

WWSD以其强大的抗封锁能力和高速连接，成为手机梯子软件中的佼佼者。

优势：

✔手机梯子加速器app：提供Android和iOS专用应用，界面友好，操作简单。

✔免费体验：新用户注册送三天免费试用。

✔高速稳定：支持YouTube、Netflix高清视频流��播放。

✔隐私保障：使用Google或ChatGPT不会触发验证。

推荐理由：WWSD在苹果手机梯子和安卓设备上表现卓越，专为中国用户优化的服务器节点（如日本、新加坡）确保翻墙顺畅，是手机梯子哪个好用的首选答案。

二、Tinny：性价比高的手机梯子

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Tinny以不限设备数量和低廉价格著称，是手机梯子vps免费体验的理想选择。

优势：

✔不限设备：一个账号支持无限设备同时连接。

✔价格实惠：最低流量套餐￥7。

✔伪装模式：Camouflage Mode和NoBorders Mode自动优化连接。

✔多协议支持：节点丰富，速度稳定。

推荐理由：Tinny的手机梯子加速器app简单易用，适合多设备用户，尤其是需要免费手机梯子替代方案的家庭或团队。

三、Newline：稳定可靠的手机梯子软件

官网地址：https://xuebispeed.com/

Newline以稳定性和中文支持闻名，是手机梯子软件中的强力选手。

优势：

✔中文支持：网站和应用提供中文界面，支持银联支付。

✔手动设置：封锁严重时可通过OpenVPN或IKEv2手动连接。

✔附加功能：可低价获取云存储和密码管理服务。

推荐理由：Newline的手机梯子加速器app在iPhone和安卓上均表现优异，手动设置功能确保极端环境下仍可使用，是苹果手机梯子的可靠选择。

四、Moon：预算友好型手机梯子

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Moon价格亲民，提供稳定的手机梯子软件服务。

优势：

✔价格低廉：多种套餐可选，性价比高。

✔24小时客服：实时提供备用连接方案。

✔31天退款：保障用户试用体验。

推荐理由：Moon适合预算有限的用户，其手机梯子加速器app为寻找免费手机梯子替代品的用户提供了实惠选择。

五、免费手机梯子推荐：小火箭加速器永久免费版

除了付费VPN，小火箭加速器永久免费版（Shadowrocket）也是一款广受欢迎的免费手机梯子工具，特别适合iOS用户。

优势：

✔永久免费：无需订阅即可使用基本功能。

✔简单配置：支持多种协议，设置方便。

✔高速连接：适合浏览网页和轻量视频。

注意事项：

免费版可能有流量限制或广告。

需要自行获取免费节点。

推荐理由：小火箭加速器永久免费版是苹果手机梯子的热门选项，适合不愿付费但需基本翻墙功能的用户。

如何选择适合你的手机梯子软件？

预算有限：优先考虑手机梯子加速器免费选项，如小火箭加速器永久免费版。

追求稳定高速：选择WWSD或Tinny。

多设备需求：Newline是不二之选。

苹果用户：苹果手机梯子可优先选WWSD或小火箭。

无论你是想了解手机梯子哪个好用，还是寻找手机梯子vps免费方案，以上推荐都能满足你的需求。

常见问题解答

1. 手机梯子软件能在哪些设备上使用？

大多数VPN提供跨平台支持，包括Windows、Mac、iPhone、安卓手机、平板、智能电视甚至路由器。

2. 苹果手机梯子如何设置？

下载VPN应用，注册账号并连接即可。部分VPN支持手动设置，适合封锁严重时使用。

3.访问哪些网站需要手机梯子加速器app？

Google、YouTube、Twitter、Facebook、ChatGPT、WhatsApp等热门网站均需翻墙访问。

在2025年的网络环境下，一款好用的手机梯子加速器免费工具至关重要。本文推荐的几款手机梯子以及小火箭加速器永久免费版，涵盖了从高端稳定到免费实用的多种选择。无论你是寻找手机梯子软件还是苹果手机梯子，希望这些建议能助你找到最佳方案，畅享无界网络！立即试用，体验属于你的手机梯子加速器app吧！