Trending March 2024 # Is Cash App Safe? What You Need To Know About Its Digital Transactions # Suggested April 2024 # Top 7 Popular

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Edgar Cervantes / Android Authority

We live in a world where financial transactions have extended beyond the walls of banks and into the virtual realm. Among various mobile payment services that have sprouted in this space, Cash App has claimed a significant user base thanks to its convenience and speed. As a peer-to-peer transaction service, it competes with popular alternatives such as Venmo and PayPal. But as with any virtual platform dealing with sensitive data, it raises an inevitable question: Is Cash App safe?

How safe is Cash App vs other payment apps?

Common Cash App scams and how to avoid them

Edgar Cervantes / Android Authority

Despite Cash App’s robust security measures, it isn’t immune to scams, with the weakest link often being unaware users. Below are some common Cash App scams to look out for:

Impersonation scams: Scammers often pretend to be Cash App support to access your account. Remember, official support will never ask for your sign-in code or PIN.

False goods/services: Beware of anyone offering expensive items in return for Cash App payment exclusively, as this is usually a scam. Remember, you can’t usually cancel Cash App transactions.

Random deposits: Unexpected deposits may be a setup for a scam. Always confirm the source of these deposits.

Prize scams: Some scammers claim you’ve won a prize but require a fee to claim it. Cash App never requires a fee for contests or promotions.

SSN scams: Do not share your Social Security number on the app unless you’re dealing with a trusted source.

Fake relief payment scams: Be wary of claims offering government grants or relief program funds in return for your financial information.

Cash-flipping scams: Avoid promises of high returns on a small investment, as these are typically scams.

Fake refund scams: Beware of buyers claiming to have made a payment multiple times, demanding a refund for an item they never actually paid for.

Romantic scams: Exercise caution when someone unknown to you expresses romantic intentions and asks for money via Cash App.

#CashAppFridays scams: Scammers may use the real #CashAppFridays promotion to trick users into giving up a payment or login information.

Fake security alert scams: Be aware of fraudulent emails claiming your Cash App account is compromised. They often include links to fake websites to steal your login information.

If you encounter anyone on Cash App who has approached you with these scams, it’s best to report and block them.

FAQs

Using Cash App carries the risk of scams or fraud, as with any digital payment platform, if users are not vigilant about security measures.

Yes, Cash App is a legitimate service owned by Square, Inc. However, users must be wary of scams and never share sensitive information.

While Cash App’s policy is not to offer refunds once a payment is made, in cases of unauthorized access, users may be eligible for refunds after investigation.

Both Cash App and Venmo use data encryption and fraud detection measures to protect users. However, Venmo offers fraud protection for authorized payments, while Cash App does not.

Cash App and Zelle both offer robust security features. However, because Zelle connects to your bank account and doesn’t store your money on the app itself, you are still covered by FDIC insurance.

Both have their strengths. PayPal has a broader global reach and extensive buyer protection, while Cash App’s interface includes the ability to invest in stocks and Bitcoin.

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Is Lastpass Safe? Here’S What You Need To Know

Joe Hindy / Android Authority

Password managers like LastPass offer to maximize your online security while also making logging into your accounts more convenient. The idea is simple — secure your vault with a single master password and generate complex random passwords for all of your other accounts. As one of the most popular password managers out there, however, is LastPass safe from attacks and should you use it?

In this article, let’s explore how password managers like LastPass work, whether they’re secure, and what it might take for an attacker to get their hands on your online credentials.

Zero knowledge encryption means that LastPass can never access your saved passwords.

That said, LastPass has recently found itself embroiled in controversy over multiple confirmed hacks and breaches. Very few password managers have reported as many successful attacks to date. Luckily, the aforementioned zero-knowledge security model has prevented attackers from accessing passwords.

Related: What is two-factor authentication and why should you use it?

How does LastPass store your passwords?

LastPass saves your usernames and passwords in an encrypted database, which is also commonly referred to as a vault. According to the company’s security disclosure, vaults are secured using 256-bit AES encryption. The key used to decrypt a vault is based on the account’s master password.

See also: What is encryption?

Even with an extremely powerful computer, a hacker would need several years, bordering on centuries, to crack a single AES-256 key. While that could change in the future, AES encryption is used to secure everything from military secrets to bank accounts.

Needless to say, it’s extremely unlikely that an attacker will brute force their way into your LastPass vault.

Does LastPass have access to your master password?

No, LastPass does not have access to your master password. And since the company doesn’t store your master password, no employee or malicious actor can decrypt the contents of your vault either.

When you sign up for an account, the app generates an encrypted vault locally on your device. The vault is then uploaded to LastPass’ servers in this encrypted state, where it’s stored as a backup. Each time you log into your account on a new device, the app fetches this backup and asks you to input your master password to unlock it.

LastPass does not store a copy of your master password.

It’s extremely important that you use a secure master password. Moreover, you should never use your LastPass master password anywhere else. Doing so dramatically increases the chances of an attacker gaining access to your password from elsewhere. From there, they can simply use it to unlock your LastPass vault.

Can LastPass be hacked?

Joe Hindy / Android Authority

LastPass is a frequent target of hackers and malicious attackers. Moreover, the company has a poor track record of warding off such attacks. While user passwords haven’t been compromised to date, the frequency of successful breaches is not a good sign for a security-focused company.

LastPass’ encryption keeps passwords safe, but you should still consider alternative password managers.

In conclusion, LastPass has never been compromised in the traditional sense — user passwords remain encrypted and safe on the platform. However, if you care about all-round security, you should definitely look for an alternative. And regardless of which password manager you choose, always enable two-factor authentication for an additional layer of security.

See also: 5 best free LastPass alternatives and how to transfer

What Is Waze? Everything You Need To Know About The Popular Navigation App

Edgar Cervantes / Android Authority

Most of us use Google Maps for getting directions, but is it the best option? Other navigation apps, such as Waze, are more community-focused and have accumulated over 140 million users since they launched. Users act as the eyes and ears on the road to provide up-to-the-minute traffic conditions. Here’s everything you need to know about Waze and why you should consider using it.

What is Waze, and how does it work? How does Waze work?

Waze collects crowd-sourced information to calculate average speeds, check for errors, and provide directions for turn-by-turn navigation. Therefore, drivers share real-time information that translates into traffic conditions and road structure.

Is Waze free?

Edgar Cervantes / Android Authority

Yes, the app is 100% free to download and use for everyone. Supported versions and devices include iOS 13 and above and Android OS 6 and above. Your device must have GPS and GSM/3G/4G/5G connectivity for the app to work correctly.

Remember that service relies on your mobile data plan to function while on the go. Continuous usage could use a lot of data, so make sure to monitor its data consumption to prevent high bills. On that note, if you’re planning to navigate abroad, be sure to ask your provider about data packages when traveling.

Who owns Waze?

Google currently owns Waze. In June 2013, the United States Federal Trade Commission (FTC) questioned whether Google’s acquisition of the company might violate competition law, given that Waze was one of the few competitors in the mobile mapping sector to Google Maps. The FTC re-examined Google’s acquisition of the company in 2023, but no changes in ownership have been effected since then.

Under Google, Waze has largely remained independent as a separate brand. Although, its 500 employees have been integrated into part of Google’s Geo organization, which oversees Google Maps alongside Earth and Street View.

Is Waze better than Google Maps?

What is Waze Carpool?

Waze Carpool was a separate mobile app for riders to match fellow commuters heading in the same direction. After six years of activity, the carpooling service was shut down in April 2023 during the COVID-19 pandemic.

When it was in operation, riders could schedule their weekly rides and stay on track with in-app reminders. The commute cost was split between riders and was often cheaper than ride-sharing services such as Uber. The company says it will focus on finding new ways to support drivers on the road and help cities address mobility problems and congestion.

FAQs

No, Waze is free to download and use. However, it does use data, so charges may apply depending on your mobile data plan.

The amount of data used depends on the device you are using, the route taken and its length, maps downloaded, time of the day, day of the week, number of reports and traffic, number of active drivers, and other factors.

Yes, users can mark the presence of an officer with a small icon and indicate if the officer is visible or hidden. Google states that knowing the whereabouts of an officer promotes safer driving.

Yes, Google acquired the company in 2013 for $966 million.

Waze is a navigation app used primarily for providing turn-by-turn directions. It incorporates real-time, crowd-sourced data to provide information about current traffic conditions, hazards, speed traps, and other obstacles on the road.

Yes, Waze remains a popular navigation app with over 150 million active users worldwide.

Yes, you can use Waze without an account for basic navigation. However, an account is required to access certain features, such as reporting traffic incidents or editing maps.

Everything You Need To Know About Nintendo And Its Consoles

Nintendo at a glance

Nintendo isn’t the only console manufacturer on the market, but it’s definitely been around the longest. First formed in 1889, the company created its first games in the 1970s before launching its first dedicated home console in 1983. Fast-forward to 2023 and the Nintendo Switch is a major sales sensation, reinforcing the company’s position in the market.

Unlike Sony and Microsoft though, Nintendo’s gaming business is its only real business, to begin with. So it doesn’t have the ability to fall back on businesses like TVs, computing, movies, and music when the going gets tough.

Nintendo consoles

Hadlee Simons / Android Authority

NES

The Nintendo Entertainment System (NES) was the company’s first proper home console, having previously launched arcade machines and the Game & Watch handheld. 1983’s NES delivered 2D visuals and support for up to 512 colors, with games coming on a cartridge.

Nintendo’s machine revived a console space that had been decimated by the videogame crash of 1983, owing to a relatively competitive price as well as quality games like Super Mario Bros, The Legend of Zelda, and Metroid.

SNES Nintendo 64

GameCube

Nintendo’s successor to the N64 was the GameCube, coming in late 2001. And its design made for a breath of fresh air compared to the serious black boxes touted by Sony and Microsoft. The GameCube instead was a purple cube, featuring powerful hardware that was easy to work with, a carry handle on its back, and a disc-based format (albeit holding 1.4GB of data) for the first time in Nintendo’s home consoles.

The GameCube saw Nintendo slide further down the rankings, as the PS2 and even the original Xbox beat it at the sales tills. In any event, people who bought the Cube were treated to top-notch wares like Metroid Prime, The Legend of Zelda: Wind Waker, Mario Kart: Double Dash, Animal Crossing, and Super Smash Bros Melee.

Wii

Nintendo’s fortunes were revitalized in late 2006 when the company launched the Wii as a follow-up to the GameCube. The new console had a modest power boost over its predecessor, but the real game-changer was the TV remote-style controller that offered motion gestures.

This simple input method meant you could swing the controller to swing a baseball bat, point the controller at a specific area on the screen to aim an in-game weapon, or conduct a bowling motion to get a strike in ten-pin bowling.

This premise meant that the Wii was the most popular console of its generation, out-selling the Xbox 360 and PlayStation 3. The Wii’s initial performance was no doubt helped by the inclusion of Wii Sports as a pack-in title, and this combo even gained popularity in some old-age homes. We also got gems like the Super Mario Galaxy games, Xenoblade Chronicles, Metroid Prime 3, and Kirby’s Epic Yarn.

Wii U

Odd name aside, 2012’s Wii U delivered an interesting concept, featuring a gamepad with a tablet-sized screen as well as supporting Wii remotes. This allowed for asynchronous gameplay, such as the remote-toting players using the TV to search for the gamepad-toting user (who was using the controller’s built-in screen). You could also use the gamepad’s small screen to play full-fledged games in case the TV was being used.

Unfortunately for Nintendo, the combo of a weird, unpolished controller (it had poor battery life and a resistive touchscreen) and underpowered internals resulted in the Wii U being the firm’s least successful home console since the Virtual Boy. It nevertheless hosted some quality games and arguably remains the best place to legally play retro games owing to backward compatibility with Wii games and the expansive Virtual Console digital service.

Switch Game Boy Advance

How do you top the Game Boy and Game Boy Color? Nintendo’s thinking was to essentially make a handheld that was more than a match for the SNES. That meant a 32-bit CPU, support for 32,768 colors, and the addition of L and R shoulder buttons. In fact, the GBA was powerful enough to run a variety of SNES ports and even a host of 3D games like Duke Nukem 3D, Doom, and more.

One particularly smart feature was backward compatibility with Game Boy and Game Boy Color games, so consumers could still play their old library of titles after upgrading to the new machine. Toss in roughly 15 hours of juice via two AA batteries and you had a really solid machine that destroyed all comers at the time.

Nintendo DS

The follow-up to the GBA saw Nintendo rip up conventions and decide that two screens were better than one. That was the premise of 2004’s Nintendo DS, featuring a clamshell design with a traditional display up top and a resistive touch-screen at the bottom. Nintendo also added extras like a stylus (complete with stylus slot), a microphone, and a second cartridge slot for backwards compatible GBA games.

This all made for a very quirky design, and the console wasn’t a runaway hit at first. But games like Brain Training, Nintendogs, Animal Crossing: Wild World, and more resulted in the console capturing a vast casual gamer market and becoming a massive sales success. Nintendo would go on to offer a variety of variants, such as the DS Lite and DSi range.

Nintendo 3DS

2011’s 3DS saw the company pick up where the DS left off, with the new console having a similar clamshell design featuring one screen up top and a touchscreen below. This also enabled backwards compatibility with legacy DS games.

But the big trick with this new handheld was glasses-free 3D visuals, giving you a cool sense of immersion and offering a slider switch so you could adjust the strength of the effect. The handheld had a respectable level of power too, even seeing ports like Metal Gear Solid 3: Snake Eater, The Legend of Zelda: Ocarina Of Time 3D, and Luigi’s Mansion.

Nintendo later offered variants like the New Nintendo 3DS range (featuring more power and an integrated right control pad), as well as the 2DS. The latter device dropped the 3D functionality and abandoned the clamshell form factor, but offered a significantly cheaper price tag.

Nintendo controllers

Unlike Sony and its PlayStations, Nintendo has generally steered clear of using the same basic controller design for most of its consoles. Instead, with a few exceptions, we’ve seen new gamepad designs for each generation.

The NES controller back in the 1980s introduced the D-pad for the first time, while also featuring Start and Select buttons and two face buttons. Nintendo would build on this with the SNES controller, bringing four face buttons in total as well as a pair of shoulder buttons. This gamepad also delivered a more rounded design as opposed to the NES controller’s sharp corners.

Nintendo hasn’t been shy about coming up with some crazy controller designs.

What would the controller for the Wii’s successor look like? Well, the Wii U would bring a huge controller that had a tablet-sized touchscreen on it (seen above). This allowed users to either get a different perspective in games or play titles on the smaller screen entirely if the TV was in use. The rest of the Wii U controller was pretty traditional, featuring two analog sticks, four shoulder triggers, four face buttons. The gamepad did however feature a selfie camera.

Nintendo’s Switch also has some radically different controller designs, as it offers two so-called Joycon controllers. These controllers enable handheld gaming when they are attached to the Switch. But slide them off and they can be used separately, such as for local multiplayer. Each controller has two shoulder triggers, an analog stick, and two more hidden shoulder buttons that are only visible when the controllers are detached from the Switch itself. These controllers still maintain motion functionality and also offer so-called HD Rumble for better vibration.

What about Nintendo accessories?

The house of Mario has sold numerous accessories for its consoles over the years. The NES got a lightgun, Robotic Operating Buddy toy, a modem, and multi-tap. But perhaps the most notable add-on was the Famicom Disk System for Japan, which was an add-on that offered disk-based games. These disks were rewritable and consumers could buy games via vending machines with their old disks.

SNES owners had quite a few accessories too, depending on their region. This included a mouse, light gun, and the Satellaview satellite modem for downloading new games and content. One noteworthy accessory was the Super Game Boy, which allowed users to play their Game Boy games via the home console.

The Nintendo 64 also had its share of accessories released throughout its lifespan, including quite a few being quirky and/or technologically interesting. Prominent accessories in this regard include the Expansion Pak (giving 4MB of extra RAM for sharper visuals or better performance), the Rumble Pak to enable controller vibration, and a microphone for voice commands in Hey You Pikachu. This console also received a Japan-only add-on dubbed the Nintendo 64DD, using proprietary rewritable disks and offering online functionality.

1-Up Studio (Mother 3, Sword of Mana)

Entertainment Planning and Development (The Legend of Zelda: Breath of the Wild, Animal Crossing: New Horizons, Splatoon 2)

Nintendo Software Technology (Mario vs Donkey Kong, Wave Race: Blue Storm)

Monolith Soft (Xenoblade Chronicles series, Project X Zone)

NDCube (Clubhouse Games, Super Mario Party)

Next Level Games (Luigi’s Mansion 3, Luigi’s Mansion: Dark Moon, Super Mario Strikers)

Retro Studios (Metroid Prime series, Donkey Kong Country: Tropical Freeze)

Notable competitors

The Kyoto company has had several major rivals over the years,  spanning both home and handheld console arenas. Some of these rivals are no longer in business, but there are still a couple of active contenders worth knowing.

Sony

Oliver Cragg / Android Authority

You could definitely argue that Sony is Nintendo’s arch-rival in the last three decades. The rivalry was actually born out of a scuppered partnership between the two in the early 1990s. Nintendo and Sony were working on a CD-based add-on for the SNES, but contractual disputes between the two companies meant that Nintendo halted the tie-up at the last minute.

Rather than let all its development work go to waste, Sony kept working on a CD-based console. This became the PlayStation, launching in 1994 in Japan and 1995 in the US. The original console would beat the Nintendo 64 in terms of global sales, while 2000’s PlayStation 2 would absolutely obliterate the competition (Nintendo’s GameCube included).

Microsoft

Oliver Cragg / Android Authority

The house of Windows was a late entrant to the console wars, joining with the original Xbox back in 2001. The company’s first effort pioneered several features that are now commonplace in the console gaming space, such as an integrated broadband adapter and hard drive.

Unbelievably, Microsoft’s first home console actually out-sold the GameCube, although it was a distant second to the all-conquering PlayStation 2. Nevertheless, this showed that the Xbox name was here to stay and that Nintendo couldn’t rest on its laurels.

Other rivals over the years

Best moments in Nintendo history

Oliver Cragg / Android Authority

The NES revives the industry

How many companies can say they’re responsible for revitalizing an entire industry? Nintendo is one of them, as the NES was launched just after the great video game crash of 1983. The crash was caused by a flood of low-quality games and a ton of consoles, resulting in the industry almost destroying itself.

But the release of the NES in the early to mid-1980s rejuvenated the industry in a massive way, owing to competitive pricing and a slew of high-quality games. It’s tough to argue that the years that followed would be as fruitful for the industry if the NES weren’t released.

Pokemon runs rampant Nintendo DS beats Sony PSP

Nintendo’s home console business was a disappointment in the early 2000s due to the flagging performance of the GameCube relative to the PS2. But one bright spot was its long-running handheld division, with the Game Boy Advance line proving to be extremely popular.

Then Sony announced and launched its first handheld, the PlayStation Portable, in 2004. It’s easy to forget right now, but there was a real feeling from many observers that Sony would beat Nintendo if it ever got into the handheld space.

The PSP indeed sold very well, but there’s no denying that the DS was more popular. According to VGChartz, the DS sold over 150 million units compared to the PSP’s 81 million. Nintendo would maintain this momentum with the 3DS, which absolutely obliterated the Vita and resulted in Sony leaving the handheld business.

Nintendo Wii destroys everything

It’s not exactly one moment, but the Wii’s massive success was a huge story from 2007 onwards. The console was hard to get at its November 2006 launch and this continued to be the case for months down the line. In fact, the machine wound up selling just over 100 million units, ahead of the PS3 and Xbox 360.

The Wii’s success was all the more satisfying due to the fact that the previous console (GameCube) had sold so poorly while the N64 also played second-fiddle to the PS1. So it represented Nintendo returning to the top of the industry.

Nintendo’s Switch is a sales sensation

It’s not necessarily one moment, but Nintendo obliterating all comers with the Switch certainly has to be up there. The company’s previous console, the Wii U, had been a disastrous commercial failure, so the pressure was on for the company to deliver on the Switch.

That’s indeed exactly what happened from 2023 onwards, as the new hybrid console quickly flew off the shelves and became tough to get. It was a very welcome change from the Wii U era, showing that Nintendo still had what it took to blow the industry away.

Worst moments in Nintendo history

Oliver Cragg / Android Authority

The Virtual Boy is a horrible failure

Nintendo would probably like to forget that the Virtual Boy ever happened. The 1995 console offered stereoscopic 3D visuals years before the 3DS would do the same. But the console proved to be a massive failure and was discontinued after less than a year.

A big part of its failure was the limited color palette, only displaying red and black colors that resulted in headaches being reported by consumers and reviewers. Then there was the weird head-mounted display, which was mounted on a stand and required you to put it on a table. No wonder it failed.

The Wii U stumbles and falls (hard)

Nintendo was fresh off the massive success of the Wii when it opted to release the radical Wii U console. Featuring a decent graphical bump over the Wii and a controller with an integrated screen, it felt like Nintendo was onto something really cool at first. And quite a few studios hopped aboard to support the machine.

But the Wii U stumbled out of the gate, and third-party developers gradually dialed back support for the console as a result. Toss in the Xbox One and PS4 out-shining Nintendo’s console, and it was a repeat of the GameCube all over again. Except the Wii U somehow sold fewer units than the purple cube.

JoyCon drift

When the Nintendo Switch was launched in March 2023, some owners quickly discovered an issue that became known as JoyCon Drift. To put it simply, the JoyCon analog stick would drift uncontrollably, meaning that an in-game character would move without you wanting to do so.

It was a disappointing flaw but what made it much worse was the fact that Nintendo didn’t make any real attempt to address the issue for a long time. It’s since revised the design of the Switch OLED variant’s JoyCon to combat this issue, but says the issue won’t ever go away as it’s related to wear-and-tear.

Ai Chatbots: What You Need To Know About Text Generation

AI chatbots have rapidly gained popularity in recent years, and their ability to generate text is one of their most important features, providing numerous benefits for businesses and individuals alike.

Artificial intelligence (AI) chatbots have the potential to change the way content is created. Text generation allows chatbots to have more natural and engaging conversations with users, and it can also be used to create a variety of other content, such as articles, blog posts, and even creative writing.

In this article, we will explore everything you need to know about AI chatbots and their text generation capabilities.

What is an AI chatbot?

An AI chatbot is a computer program that uses artificial intelligence (AI) to simulate conversations with human users. AI chatbots are often used in customer service applications, where they can answer questions, provide support, and resolve issues. They can also be used for marketing and sales, education, and entertainment.

AI chatbots are trained on large datasets of text and code, which allows them to learn how to understand and respond to human language. They use natural language processing (NLP) to understand the meaning of human language, and machine learning to generate responses that are relevant and helpful.

AI chatbots are still under development, but they have the potential to revolutionize the way we interact with computers. They can provide a more personalized and engaging experience for users, and they can help businesses to improve efficiency and customer satisfaction.

Here are some of the benefits of using AI chatbots:

Improved customer service: AI chatbots can provide 24/7 customer service, which can help businesses to improve customer satisfaction.

Reduced costs: AI chatbots can help businesses to reduce costs by automating tasks that would otherwise be done by human employees.

Increased productivity: AI chatbots can help businesses to increase productivity by freeing up human employees to focus on more complex tasks.

Improved data collection: AI chatbots can collect data about customer interactions, which can be used to improve products and services.

How do AI chatbots generate text?

There are a few different ways that AI chatbots can generate text. One common approach is to use natural language processing (NLP). NLP is a field of artificial intelligence that deals with the interaction between computers and human (natural) languages. NLP techniques can be used to understand the meaning of human language, and to generate text that is both grammatically correct and relevant to the context.

Another common approach is to use machine learning (ML). Machine learning is a field of artificial intelligence that deals with the development of algorithms that can learn from data. Machine learning techniques can be used to train AI chatbots to generate text that is similar to the text that they have been trained on.

Finally, AI chatbots can also be used to generate creative text. This can be done by using techniques such as natural language generation (NLG) and deep learning. NLG is a field of artificial intelligence that deals with the generation of natural language text. Deep learning is a type of machine learning that uses artificial neural networks to learn from data.

AI chatbots that are able to generate creative text can be used for a variety of purposes, such as writing stories, poems, and scripts. They can also be used to generate text that is tailored to a specific audience, such as marketing copy or customer service responses.

As AI technology continues to develop, AI chatbots will become even more sophisticated and capable. They will be able to generate text that is more natural, more creative, and more relevant to the context. This will make them even more useful for a variety of applications.

What are the different types of AI chatbots?

There are many different types of AI chatbots, but they can generally be divided into two categories: rule-based chatbots and machine learning chatbots.

Rule-based chatbots are the simplest type of AI chatbot. They are programmed with a set of rules that define how they should respond to certain prompts or questions. For example, a rule-based chatbot might be programmed to answer questions about the weather, provide customer service information, or make appointments.

Machine learning chatbots are more sophisticated than rule-based chatbots. They are trained on large datasets of text and code, which allows them to learn how to understand and respond to human language in a more natural way. Machine learning chatbots can be used for a variety of tasks, such as customer service, sales, and marketing.

Here are the pros and cons of the two main types of AI chatbots:

Rule-based chatbots

Pros:

Simple to develop and maintain

Can be used for a variety of tasks

Relatively inexpensive

Cons:

Can be limited in their ability to understand and respond to complex questions

May not be able to learn and adapt over time

Machine learning chatbots

Pros:

Can understand and respond to complex questions

Can learn and adapt over time

Can be used for a variety of tasks

Cons:

More complex to develop and maintain

Can be more expensive

May not be able to handle all types of requests

Ultimately, the best type of AI chatbot for a particular application will depend on the specific needs of the user.

Here are some other types of AI chatbots:

Hybrid chatbots combine the features of rule-based and machine learning chatbots. They are programmed with a set of rules, but they can also learn and adapt over time.

Virtual assistants are AI chatbots that are designed to provide assistance to users. They can be used to answer questions, make appointments, and perform other tasks.

Chatbots for education are used to provide instruction and support to students. They can also be used to assess student learning and provide feedback.

Chatbots for entertainment are used to provide games, quizzes, and other forms of entertainment. They can also be used to connect users with each other.

Comparison of Accuracy, Fluency, Creativity, Relevance, and Plagiarism

AI chatbots are becoming increasingly sophisticated, and their ability to generate text is becoming more impressive. However, there are still some key differences between the text generated by AI chatbots and the text generated by humans.

here is a comparison of text generated by AI chatbots:

Accuracy: One of the biggest differences is accuracy. AI chatbots can be very accurate, but they can also make mistakes. It is important to evaluate the accuracy of an AI chatbot before using it for important tasks.

Fluency: Another difference is fluency. AI chatbots should be able to generate text that is fluent and easy to read. If the text is choppy or difficult to understand, it may be a sign that the AI chatbot is not very good. It is important to choose a chatbot that can generate text that is fluent and easy to read.

Creativity: The creativity of text generated by AI chatbots can also vary. Some chatbots are able to generate text that is original and thought-provoking, This can be a great asset for businesses that want to create engaging content for their customers, while others produce text that is repetitive or unoriginal. The creativity of the text will depend on the chatbot’s training data and the complexity of the text.

Relevance: The relevance of text generated by AI chatbots also varies. AI chatbots should be able to generate text that is relevant to the topic at hand. If the text is off-topic or irrelevant, it may be a sign that the AI chatbot is not very good.

Plagiarism: Plagiarism is a concern with AI chatbots. They can sometimes plagiarize text from other sources. This is a problem because it can lead to legal problems for businesses that use AI chatbots. It is important to use an AI chatbot that has a good plagiarism detection system.

Ultimately, the best AI chatbot for a particular application will depend on the specific needs of the user. If you are looking for an AI chatbot that is accurate, fluent, creative, relevant, and free of plagiarism, then you will need to do some research to find the right one.

Factors to consider when choosing an AI chatbot

There are many factors to consider when choosing an AI chatbot. Some of the most important factors include:

Purpose of the chatbot: What do you want the chatbot to do? Do you want it to answer questions, provide customer service, or something else?

Target audience: Who will be using the chatbot? What are their needs and expectations?

Features: What features are important to you? Do you need the chatbot to be able to answer questions, generate text, or translate languages?

Budget: How much are you willing to spend on a chatbot? AI chatbots can range in price from free to thousands of dollars per month.

Level of customization: Do you want a chatbot that is customized to your specific needs?

Ease of use: How easy is it to use the chatbot? Can users easily find the information they need?

Security features: Does the chatbot have security features in place to protect user data?

Support: Does the chatbot provider offer support? If so, what kind of support is available?

Once you have considered these factors, you can start to narrow down your choices and choose the best AI chatbot for your needs.

Here are some additional tips for choosing an AI chatbot:

Talk to a provider: If you have any questions, don’t hesitate to talk to a chatbot provider. They can help you choose the right chatbot for your needs.

Which AI chatbot is the best?

There is no one “best” AI chatbot, as the best chatbot for you will depend on your specific needs and requirements. However, some of the most popular and well-regarded AI chatbots include:

ChatGPT: ChatGPT is a chatbot developed by OpenAI that is known for its accuracy and fluency. It can generate text that is factually accurate and consistent with the information that it has been trained on.

Bard: Bard is a chatbot developed by Google AI that is known for its creativity and relevance. It can generate text that is original and thought-provoking, and it can also generate text that is relevant to the topic at hand.

Xiaoice: Xiaoice is a chatbot developed by Microsoft that is known for its popularity in China. It has been used by over 600 million people, and it has been praised for its ability to provide companionship and support.

YouChat: YouChat is an artificial intelligence-powered search assistant developed by chúng tôi It is designed to improve web searching using a chat-based interface that allows users to ask questions and receive relevant responses in a conversational manner. YouChat is based on large language models and machine learning algorithms that allow it to understand and interpret natural language queries and generate accurate and informative responses. It can answer general questions, explain concepts, suggest ideas, translate, summarize text, compose emails, and even write code snippets. YouChat is constantly learning and improving based on user feedback, and aims to provide a more efficient and interactive way of searching and accessing information on the internet.

The best way to choose an AI chatbot is to try out a few different ones and see which one works best for y

It is important to note that AI chatbots are still under development, and they are not perfect. They can sometimes make mistakes, and they may not always be able to understand your requests. It is important to be patient with AI chatbots, and to provide them with feedback so that they can learn and improve.

Comparison of Text Generated by ChatGPT and Bard AI

FeatureChatGPTBardAccuracyChatGPT is generally accurate, but it can sometimes make mistakes. For example, it may misspell words or make grammatical chúng tôi is very accurate, and it rarely makes mistakes.FluencyChatGPT’s text is generally fluent and easy to read. However, it can sometimes be choppy or difficult to understand.Bard’s text is very fluent and easy to read. It is always clear and concise.CreativityChatGPT can be creative, but it is not as creative as Bard. Bard is able to generate text that is original and chúng tôi is very creative, and it is able to generate text that is original and thought-provoking.RelevanceChatGPT’s text is generally relevant to the topic at hand. However, it can sometimes go off on tangents or introduce irrelevant information.Bard’s text is always relevant to the topic at hand. It is never off-topic or irrelevant.PlagiarismChatGPT can sometimes plagiarize text from other sources. This is a problem because it can lead to legal problems for businesses that use chúng tôi has a good plagiarism detection system, and it never plagiarizes text from other sources.

Overall, Bard is a more accurate, fluent, creative, relevant, and plagiarism-free chatbot than ChatGPT. If you are looking for a chatbot that can generate high-quality text, Bard is the best option.

Future of AI chatbots

The future of AI chatbots looks very promising. Chatbots have already become a popular way to automate customer service and provide quick and efficient responses to common questions.

As AI chatbots continue to improve, they may also become better at understanding human emotions and providing more personalized interactions. However, developing ethical and responsible AI solutions will continue to be an important consideration in the development and implementation of AI chatbots.

Everything You Need To Know About Scikit

Introduction

Scikit-learn is one Python library we all inevitably turn to when we’re building machine learning models. I’ve built countless models using this wonderful library and I’m sure all of you must have as well.

There’s no question – scikit-learn provides handy tools with easy-to-read syntax. Among the pantheon of popular Python libraries, scikit-learn ranks in the top echelon along with Pandas and NumPy. These three Python libraries provide a complete solution to various steps of the machine learning pipeline.

I love the clean, uniform code and functions that scikit-learn provides. It makes it really easy to use other techniques once we have mastered one. The excellent documentation is the icing on the cake as it makes a lot of beginners self-sufficient with building machine learning models.

The developers behind scikit-learn have come up with a new version (v0.22) that packs in some major updates. I’ll unpack these features for you in this article and showcase what’s under the hood through Python code.

Note: Looking to learn Python from scratch? This free course is the perfect starting point!

Table of Contents

Getting to Know Scikit-Learn

A Brief History of Scikit-Learn

Scikit-Learn v0.22 Updates (with Python implementation)

Stacking Classifier and Regressor

Permutation-Based Feature Importance

Multi-class Support for ROC-AUC

kNN-Based Imputation

Tree Pruning

Getting to Know Scikit-Learn

This library is built upon the SciPy (Scientific Python) library that you need to install before you can use scikit-learn. It is licensed under a permissive simplified BSD license and is distributed under many Linux distributions, encouraging academic and commercial use.

Overall, scikit-learn uses the following libraries behind the scenes:

NumPy: n-dimensional array package

SciPy: Scientific computing Library

Matplotlib:  Plotting Library

iPython: Interactive python (for Jupyter Notebook support)

SymPy: Symbolic mathematics

Pandas: Data structures, analysis, and manipulation

Lately, scikit-learn has reorganized and restructured its functions & packages into six main modules:

Classification: Identifying which category an object belongs to

Regression: Predicting a continuous-valued attribute associated with an object

Clustering: For grouping unlabeled data

Dimensionality Reduction: Reducing the number of random variables to consider

Model Selection: Comparing, validating and choosing parameters and models

Preprocessing: Feature extraction and normalization

scikit-learn provides the functionality to perform all the steps from preprocessing, model building, selecting the right model, hyperparameter tuning, to frameworks for interpreting machine learning models.

Scikit-learn Modules (Source: Scikit-learn Homepage)

A Brief History of Scikit-learn

Scikit-learn has come a long way from when it started back in 2007 as scikits.learn. Here’s a cool trivia for you – scikit-learn was a Google Summer of Code project by David Cournapeau!

This was taken over and rewritten by Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent Michel, all from the French Institute for Research in Computer Science and Automation and its first public release took place in 2010.

Since then, it has added a lot of features and survived the test of time as the most popular open-source machine learning library across languages and frameworks. The below infographic, prepared by our team, illustrates a brief timeline of all the scikit-learn features along with their version number:

The above infographics show the release of features since its inception as a public library for implementing Machine Learning Algorithms from 2010 to 2023

Today, Scikit-learn is being used by organizations across the globe, including the likes of Spotify, JP Morgan, chúng tôi Evernote, and many more. You can find the complete list here with testimonials I believe this is just the tip of the iceberg when it comes to this library’s popularity as there will a lot of small and big companies using scikit-learn at some stage of prototyping models.

The latest version of scikit-learn, v0.22, has more than 20 active contributors today. v0.22 has added some excellent features to its arsenal that provide resolutions for some major existing pain points along with some fresh features which were available in other libraries but often caused package conflicts.

We will cover them in detail here and also dive into how to implement them in Python.

Scikit-Learn v0.22 Updates

Along with bug fixes and performance improvements, here are some new features that are included in scikit-learn’s latest version.

Stacking Classifier & Regressor

Stacking is an ensemble learning technique that uses predictions from multiple models (for example, decision tree, KNN or SVM) to build a new model.

This model is used for making predictions on the test set. Below is a step-wise explanation I’ve taken from this excellent article on ensemble learning for a simple stacked ensemble:

The base model (in this case, decision tree) is then fitted on the whole train dataset

This model is used to make final predictions on the test prediction set

The mlxtend library provides an API to implement Stacking in Python. Now, sklearn, with its familiar API can do the same and it’s pretty intuitive as you will see in the demo below. You can either import StackingRegressor & StackingClassifier depending on your use case:

from

sklearn.linear_model

import

LogisticRegression

from sklearn.ensemble import RandomForestClassifier from chúng tôi import DecisionTreeClassifier

from

sklearn.ensemble

import

StackingClassifier

from

sklearn.model_selection

import

train_test_split

X

,

y

=

load_iris

(

return_X_y

=

True

)

estimators

=

[

(

'rf'

,

RandomForestClassifier

(

n_estimators

=

10

,

random_state

=

42

)),

(

'dt'

,

DecisionTreeClassifier

(

random_state

=

42

)

)

]

clf

=

StackingClassifier

(

estimators

=

estimators

,

final_estimator

=

LogisticRegression

()

)

X_train

,

X_test

,

y_train

,

y_test

=

train_test_split

(

X

,

y

,

stratify

=

y

,

random_state

=

42

)

clf

.

fit

(

X_train

,

y_train

)

.

score

(

X_test

,

y_test

)

Permutation-Based Feature Importance

As the name suggests, this technique provides a way to assign importance to each feature by permuting each feature and capturing the drop in performance.

But what does permuting mean here? Let us understand this using an example.

Let’s say we are trying to predict house prices and have only 2 features to work with:

LotArea – (Sq Feet area of the house)

YrSold (Year when it was sold)

The test data has just 10 rows as shown below:

Next, we fit a simple decision tree model and get an R-Squared value of 0.78. We pick a feature, say LotArea, and shuffle it keeping all the other columns as they were:

Next, we calculate the R-Squared once more and it comes out to be 0.74. We take the difference or ratio between the 2 (0.78/0.74 or 0.78-0.74), repeat the above steps, and take the average to represent the importance of the LotArea feature.

We can perform similar steps for all the other features to get the relative importance of each feature. Since we are using the test set here to evaluate the importance values, only the features that help the model generalize better will fare better.

Earlier, we had to implement this from scratch or import packages such as ELI5. Now, Sklearn has an inbuilt facility to do permutation-based feature importance. Let’s get into the code to see how we can visualize this:



As you can see in the above box plot, there are 3 features that are relatively more important than the other 4. You can try this with any model, which makes it a model agnostic interpretability technique. You can read more about this machine learning interpretability concept here.

Multiclass Support for ROC-AUC

The ROC-AUC score for binary classification is super useful especially when it comes to imbalanced datasets. However, there was no support for Multi-Class classification till now and we had to manually code to do this. In order to use the ROC-AUC score for multi-class/multi-label classification, we would need to binarize the target first.

Currently, sklearn has support for two strategies in order to achieve this:

from sklearn.datasets import load_iris  from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score X, y = load_iris(return_X_y=True) rf = RandomForestClassifier(random_state=44, max_depth=2) rf.fit(X,y) print(roc_auc_score(y, rf.predict_proba(X), multi_class='ovo'))

Also, there is a new plotting API that makes it super easy to plot and compare ROC-AUC curves from different machine learning models. Let’s see a quick demo:

from

sklearn.model_selection

import

train_test_split

from

sklearn.svm

import

SVC

from

sklearn.metrics

import

plot_roc_curve

from

sklearn.ensemble

import

RandomForestClassifier

from

sklearn.datasets

import

make_classification

import

matplotlib.pyplot

as

plt

X

,

y

=

make_classification

(

random_state

=5

)

X_train

,

X_test

,

y_train

,

y_test

=

train_test_split

(

X

,

y

,

random_state

=

42

)

svc

=

SVC

(

random_state

=

42

)

svc

.

fit

(

X_train

,

y_train

)

rfc

=

RandomForestClassifier

(

random_state

=

42

)

rfc

.

fit

(

X_train

,

y_train

)

svc_disp

=

plot_roc_curve

(

svc

,

X_test

,

y_test

)

rfc_disp

=

plot_roc_curve

(

rfc

,

X_test

,

y_test

,

ax

=

svc_disp

.

ax_

)

rfc_disp

.

figure_

.

suptitle

(

"ROC curve comparison"

)

plt

.

show

()

In the above figure, we have a comparison of two different machine learning models, namely Support Vector Classifier & Random Forest. Similarly, you can plot the AUC-ROC curve for more machine learning models and compare their performance.

kNN-Based Imputation

In kNN based imputation method, the missing values of an attribute are imputed using the attributes that are most similar to the attribute whose values are missing. The assumption behind using kNN for missing values is that a point value can be approximated by the values of the points that are closest to it, based on other variables.

The k-nearest neighbor can predict both qualitative & quantitative attributes

Creation of predictive machine learning model for each attribute with missing data is not required

Correlation structure of the data is taken into consideration

Scikit-learn supports kNN-based imputation using the Euclidean distance method. Let’s see a quick demo:

import

numpy

as

np

from

sklearn.impute

import

KNNImputer

X

=

[[4

,

6

,

np

.

nan

],

[

3

,

4

,

3

],

[

np

.

nan

,

6

,

5

],

[

8

,

8

,

9

]]

imputer

=

KNNImputer

(

n_neighbors

=

2

)

print

(

imputer

.

fit_transform

(

X

))

You can read about how kNN works in comprehensive detail here.

Tree Pruning

In basic terms, pruning is a technique we use to reduce the size of decision trees thereby avoiding overfitting. This also extends to other tree-based algorithms such as Random Forests and Gradient Boosting. These tree-based machine learning methods provide parameters such as min_samples_leaf and max_depth to prevent a tree from overfitting.

Pruning provides another option to control the size of a tree. XGBoost & LightGBM have pruning integrated into their implementation. However, a feature to manually prune trees has been long overdue in Scikit-learn (R already provides a similar facility as a part of the rpart package).

In its latest version, Scikit-learn provides this pruning functionality making it possible to control overfitting in most tree-based estimators once the trees are built. For details on how and why pruning is done, you can go through this excellent tutorial on tree-based methods by Sunil. Let’s look at a quick demo now:

from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import make_classification X

,

y

=

make_classification

(

random_state

=

0

)

rf

=

RandomForestClassifier

(

random_state

=

0

,

ccp_alpha

=

0

)

.

fit

(

X

,

y

)

print

(

"Average number of nodes without pruning

{:.1f}

"

.

format

(

np

.

mean

([

e

.

tree_

.

node_count

for

e

in

rf

.

estimators_

])))

rf

=

RandomForestClassifier

(

random_state

=

0

,

ccp_alpha

=

0.1

)

.

fit

(

X

,

y

)

print

(

"Average number of nodes with pruning

{:.1f}

"

.

format

(

np

.

mean

([

e

.

tree_

.

node_count

for

e

in

rf

.

estimators_

])))

End Notes

The scikit-learn package is the ultimate go-to library for building machine learning models. It is the first machine learning-focused library all newcomers lean on to guide them through their initial learning process. And even as a veteran, I often find myself using it to quickly test out a hypothesis or solution I have in mind.

The latest release definitely has some significant upgrades as we just saw. It’s definitely worth exploring on your own and experimenting using the base I have provided in this article.

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