His story will inspire you.

“Google search went down for a few hours in 2002; Jeff Dean handled all the queries by hand and checked quality doubled.”- Jeff Dean Facts.

One of many Jeff Dean jokes, but you get the idea.

Google's top six engineers met in a war room in mid-2000. Google's crawling system, which indexed the Web, stopped working. Users could still enter queries, but results were five months old.

Google just signed a deal with Yahoo to power a ten-times-larger search engine. Tension rose. It was crucial. If they failed, the Yahoo agreement would likely fall through, risking bankruptcy for the firm. Their efforts could be lost.

A rangy, tall, energetic thirty-one-year-old man named Jeff dean was among those six brilliant engineers in the makeshift room. He had just left D. E. C. a couple of months ago and started his career in a relatively new firm Google, which was about to change the world. He rolled his chair over his colleague Sanjay and sat right next to him, cajoling his code like a movie director. The history started from there.

When you think of people who shaped the World Wide Web, you probably picture founders and CEOs like Larry Page and Sergey Brin, Marc Andreesen, Tim Berners-Lee, Bill Gates, and Mark Zuckerberg. They’re undoubtedly the brightest people on earth.

Under these giants, legions of anonymous coders work at keyboards to create the systems and products we use. These computer workers are irreplaceable.

Let's get to know him better.

It's possible you've never heard of Jeff Dean. He's American. Dean created many behind-the-scenes Google products. Jeff, co-founder and head of Google's deep learning research engineering team, is a popular technology, innovation, and AI keynote speaker.

While earning an MS and Ph.D. in computer science at the University of Washington, he was a teaching assistant, instructor, and research assistant. Dean joined the Compaq Computer Corporation Western Research Laboratory research team after graduating.

Jeff co-created ProfileMe and the Continuous Profiling Infrastructure for Digital at Compaq. He co-designed and implemented Swift, one of the fastest Java implementations. He was a senior technical staff member at mySimon Inc., retrieving and caching electronic commerce content.

Dean, a top young computer scientist, joined Google in mid-1999. He was always trying to maximize a computer's potential as a child.

An expert

His high school program for processing massive epidemiological data was 26 times faster than professionals'. Epi Info, in 13 languages, is used by the CDC. He worked on compilers as a computer science Ph.D. These apps make source code computer-readable.

Dean never wanted to work on compilers forever. He left Academia for Google, which had less than 20 employees. Dean helped found Google News and AdSense, which transformed the internet economy. He then addressed Google's biggest issue, scaling.

Growing Google faced a huge computing challenge. They developed PageRank in the late 1990s to return the most relevant search results. Google's popularity slowed machine deployment.

Dean solved problems, his specialty. He and fellow great programmer Sanjay Ghemawat created the Google File System, which distributed large data over thousands of cheap machines.

These two also created MapReduce, which let programmers handle massive data quantities on parallel machines. They could also add calculations to the search algorithm. A 2004 research article explained MapReduce, which became an industry sensation.

Several revolutionary inventions

Dean's other initiatives were also game-changers. BigTable, a petabyte-capable distributed data storage system, was based on Google File. The first global database, Spanner, stores data on millions of servers in dozens of data centers worldwide.

It underpins Gmail and AdWords. Google Translate co-founder Jeff Dean is surprising. He contributes heavily to Google News. Dean is Senior Fellow of Google Research and Health and leads Google AI.

Recognitions

The National Academy of Engineering elected Dean in 2009. He received the 2009 Association for Computing Machinery fellowship and the 2016 American Academy of Arts and Science fellowship. He received the 2007 ACM-SIGOPS Mark Weiser Award and the 2012 ACM-Infosys Foundation Award. Lists could continue.

A sneaky question may arrive in your mind: How much does this big brain earn? Well, most believe he is one of the highest-paid employees at Google. According to a survey, he is paid $3 million a year.

He makes espresso and chats with a small group of Googlers most mornings. Dean steams milk, another grinds, and another brews espresso. They discuss families and technology while making coffee. He thinks this little collaboration and idea-sharing keeps Google going.

“Some of us have been working together for more than 15 years,” Dean said. “We estimate that we’ve collectively made more than 20,000 cappuccinos together.”

We all know great developers and software engineers. It may inspire many.

Many of us have struggled for years to master a second language (in my case, English). Because (at least in my situation) we've always used an input-based system or method.

I'll explain in detail, but briefly: We can understand some conversations or sentences (since we've trained), but we can't give sophisticated answers or speak fluently (because we have NOT trained at all).

What exactly is input-based learning?

Reading, listening, writing, and speaking are key language abilities (if you look closely at that list, it seems that people tend to order them in this way: inadvertently giving more priority to the first ones than to the last ones).

These talents fall under two learning styles:

• Reading and listening are input-based activities (sometimes referred to as receptive skills or passive learning).

• Writing and speaking are output-based tasks (also known as the productive skills and/or active learning).

What's the best learning style? To learn a language, we must master four interconnected skills. The difficulty is how much time and effort we give each.

According to Shion Kabasawa's books The Power of Input: How to Maximize Learning and The Power of Output: How to Change Learning to Outcome (available only in Japanese), we spend 7:3 more time on Input Based skills than Output Based skills when we should be doing the opposite, leaning more towards Output (Input: Output->3:7).

I can't tell you how he got those numbers, but I think he's not far off because, for example, think of how many people say they're learning a second language and are satisfied bragging about it by only watching TV, series, or movies in VO (and/or reading a book or whatever) their Input is: 7:0 output!

You can't be good at a sport by watching TikTok videos about it; you must play.

“being pushed to produce language puts learners in a better position to notice the ‘gaps’ in their language knowledge”, encouraging them to ‘upgrade’ their existing interlanguage system. And, as they are pushed to produce language in real time and thereby forced to automate low-level operations by incorporating them into higher-level routines, it may also contribute to the development of fluency. — Scott Thornbury (P is for Push)

How may I practice output-based learning more?

I know that listening or reading is easy and convenient because we can do it on our own in a wide range of situations, even during another activity (although, as you know, it's not ideal), writing can be tedious/boring (it's funny that we almost always excuse ourselves in the lack of ideas), and speaking requires an interlocutor. But we must leave our comfort zone and modify our thinking to go from 3:7 to 7:3. (or at least balance it better to something closer). Gradually.

“You don’t have to do a lot every day, but you have to do something. Something. Every day.” — Callie Oettinger (Do this every day)

We can practice speaking like boxers shadow box.

Speaking out loud strengthens the mind-mouth link (otherwise, you will still speak fluently in your mind but you will choke when speaking out loud). This doesn't mean we should talk to ourselves on the way to work, while strolling, or on public transportation. We should try to do it without disturbing others, such as explaining what we've heard, read, or seen (the list is endless: you can TALK about what happened yesterday, your bedtime book, stories you heard at the office, that new kitten video you saw on Instagram, an experience you had, some new fact, that new boring episode you watched on Netflix, what you ate, what you're going to do next, your upcoming vacation, what’s trending, the news of the day)

Who will correct my grammar, vocabulary, or pronunciation with an imagined friend? We can't have everything, but tools and services can help [1].

Lack of bravery

Fear of speaking a language different than one's mother tongue in front of native speakers is global. It's easier said than done, because strangers, not your friends, will always make fun of your accent or faults. Accept it and try again. Karma will prevail.

Perfectionism is a trap. Stop self-sabotaging. Communication is key (and for that you have to practice the Output too ).

“Don’t forget to have fun and enjoy the process.” — Ruri Ohama

[1] Grammarly, Deepl, Google Translate, etc.

He explained everything.

A drinking buddy confessed. Alexander. He says he works at a startup based on a scam, which appears too clever to be a lie.

Alexander (assuming he developed the story) or the startup's creator must have been a genius.

This is the story of an Internet scam that targets older individuals and generates tens of millions of dollars annually.

The business sells authentic things at 10% of their market value. This firm cannot be lucrative, but the entrepreneur has a plan: monthly subscriptions to a worthless service.

The firm can then charge the customer's credit card to settle the gap. The buyer must subscribe without knowing it. What's their strategy?

How does the con operate?

Imagine a website with a split homepage. On one page, the site offers an attractive goods at a ridiculous price (from 1 euro to 10% of the product's market worth).

Same product, but with a stupid monthly subscription. Business is unsustainable. They buy overpriced products and resell them too cheaply, hoping customers will subscribe to a useless service.

No customer will want this service. So they create another illegal homepage that hides the monthly subscription offer. After an endless scroll, a box says Yes, I want to subscribe to a service that costs x dollars per month.

Unchecking the checkbox bugs. When a customer buys a product on this page, he's enrolled in a monthly subscription. Not everyone should see it because it's illegal. So what does the startup do?

A page that varies based on the sort of website visitor, a possible consumer or someone who might be watching the startup's business

Startup technicians make sure the legal page is displayed when the site is accessed normally. Typing the web address in the browser, using Google, etc. The page crashes when buying a goods, preventing the purchase.

This avoids the startup from selling a product at a loss because the buyer won't subscribe to the worthless service and charge their credit card each month.

The illegal page only appears if a customer clicks on a Google ad, indicating interest in the offer.

Alexander says that a banker, police officer, or anyone else who visits the site (maybe for control) will only see a valid and buggy site as purchases won't be possible.

The latter will go to the site in the regular method (by typing the address in the browser, using Google, etc.) and not via an online ad.

Those who visit from ads are likely already lured by the site's price. They'll be sent to an illegal page that requires a subscription.

Laziness is humanity's secret weapon. The ordinary person ignores tiny monthly credit card charges. The subscription lasts around a year before the customer sees an unexpected deduction.

After-sales service (ASS) is useful in this situation.

After-sales assistance begins when a customer notices slight changes on his credit card, usually a year later.

The customer will search Google for the direct debit reference. How he'll complain to after-sales service.

It's crucial that ASS appears in the top 4/5 Google search results. This site must be clear, and offer chat, phone, etc., he argues.

The pigeon must be comforted after waking up. The customer learns via after-sales service that he subscribed to a service while buying the product, which justifies the debits on his card.

The customer will then clarify that he didn't intend to make the direct debits. The after-sales care professional will pretend to listen to the customer's arguments and complaints, then offer to unsubscribe him for free because his predicament has affected him.

In 99% of cases, the consumer is satisfied since the after-sales support unsubscribed him for free, and he forgets the debited amounts.

The remaining 1% is split between 0.99% who are delighted to be reimbursed and 0.01%. We'll pay until they're done. The customer should be delighted, not object or complain, and keep us beneath the radar (their situation is resolved, the rest, they don’t care).

It works, so we expand our thinking.

Startup has considered industrialization. Since this fraud is working, try another. Automate! So they used a site generator (only for product modifications), underpaid phone operators for after-sales service, and interns for fresh product ideas.

The company employed a data scientist. This has allowed the startup to recognize that specific customer profiles can be re-registered in the database and that it will take X months before they realize they're subscribing to a worthless service. Customers are re-subscribed to another service, then unsubscribed before realizing it.

Alexander took months to realize the deception and leave. Lawyers and others apparently threatened him and former colleagues who tried to talk about it.

The startup would have earned prizes and competed in contests. He adds they can provide evidence to any consumer group, media, police/gendarmerie, or relevant body. When I submitted my information to the FBI, I was told, "We know, we can't do much.", he says.

Recent investigations have shown that generative AI can boost hackers and misinformation spreaders.

Since its inception in late November 2022, OpenAI's ChatGPT has entertained and assisted many online users in writing, coding, task automation, and linguistic translation. Given this versatility, it is maybe unsurprising but nonetheless regrettable that fraudsters and mis-, dis-, and malinformation (MDM) spreaders are also considering ChatGPT and related AI models to streamline and improve their operations.

Malign actors may benefit from ChatGPT, according to a WithSecure research. ChatGPT promises to elevate unlawful operations across many attack channels. ChatGPT can automate spear phishing attacks that deceive corporate victims into reading emails from trusted parties. Malware, extortion, and illicit fund transfers can result from such access.

ChatGPT's ability to simulate a desired writing style makes spear phishing emails look more genuine, especially for international actors who don't speak English (or other languages like Spanish and French).

This technique could let Russian, North Korean, and Iranian state-backed hackers conduct more convincing social engineering and election intervention in the US. ChatGPT can also create several campaigns and various phony online personas to promote them, making such attacks successful through volume or variation. Additionally, image-generating AI algorithms and other developing techniques can help these efforts deceive potential victims.

Hackers are discussing using ChatGPT to install malware and steal data, according to a Check Point research. Though ChatGPT's scripts are well-known in the cyber security business, they can assist amateur actors with little technical understanding into the field and possibly develop their hacking and social engineering skills through repeated use.

Additionally, ChatGPT's hacking suggestions may change. As a writer recently indicated, ChatGPT's ability to blend textual and code-based writing might be a game-changer, allowing the injection of innocent content that would subsequently turn out to be a malicious script into targeted systems. These new AI-powered writing- and code-generation abilities allow for unique cyber attacks, regardless of viability.

OpenAI fears ChatGPT usage. OpenAI, Georgetown University's Center for Security and Emerging Technology, and Stanford's Internet Observatory wrote a paper on how AI language models could enhance nation state-backed influence operations. As a last resort, the authors consider polluting the internet with radioactive or misleading data to ensure that AI language models produce outputs that other language models can identify as AI-generated. However, the authors of this paper seem unaware that their "solution" might cause much worse MDM difficulties.

Literally False News

The public argument about ChatGPTs content-generation has focused on originality, bias, and academic honesty, but broader global issues are at stake. ChatGPT can influence public opinion, troll individuals, and interfere in local and national elections by creating and automating enormous amounts of social media material for specified audiences.

ChatGPT's capacity to generate textual and code output is crucial. ChatGPT can write Python scripts for social media bots and give diverse content for repeated posts. The tool's sophistication makes it irrelevant to one's language skills, especially English, when writing MDM propaganda.

I ordered ChatGPT to write a news piece in the style of big US publications declaring that Ukraine is on the verge of defeat in its fight against Russia due to corruption, desertion, and exhaustion in its army. I also gave it a fake reporter's byline and an unidentified NATO source's remark. The outcome appears convincing:

Worse, terrible performers can modify this piece to make it more credible. They can edit the general's name or add facts about current wars. Furthermore, such actors can create many versions of this report in different forms and distribute them separately, boosting its impact.

In this example, ChatGPT produced a news story regarding (fictional) greater moviegoer fatality rates:

Editing this example makes it more plausible. Dr. Jane Smith, the putative author of the medical report, might be replaced with a real-life medical person or a real victim of this supposed medical hazard.

Can deceptive texts be found? Detecting AI text is behind AI advancements. Minor AI-generated text alterations can upset these technologies.

Some OpenAI individuals have proposed covert methods to watermark AI-generated literature to prevent its abuse. AI models would create information that appears normal to humans but would follow a cryptographic formula that would warn other machines that it was AI-made. However, security experts are cautious since manually altering the content interrupts machine and human detection of AI-generated material.

How to Prepare

Cyber security and IT workers can research and use generative AI models to fight spear fishing and extortion. Governments may also launch MDM-defence projects.

In election cycles and global crises, regular people may be the most vulnerable to AI-produced deceit. Until regulation or subsequent technical advances, individuals must recognize exposure to AI-generated fraud, dating scams, other MDM activities.

A three-step verification method of new material in suspicious emails or social media posts can help identify AI content and manipulation. This three-step approach asks about the information's distribution platform (is it reliable? ), author (is the reader familiar with them? ), and plausibility given one's prior knowledge of the topic.

Consider a report by a trusted journalist that makes shocking statements in their typical manner. AI-powered fake news may be released on an unexpected platform, such as a newly created Facebook profile. However, if it links to a known media source, it is more likely to be real.

Though hard and subjective, this verification method may be the only barrier against manipulation for now.

AI language models:

How to Recognize an AI-Generated Article ChatGPT, the popular AI-powered chatbot, can and likely does generate medium.com-style articles.

AI-Generated Text Detectors Fail. Do This. Online tools claim to detect ChatGPT output. Even with superior programming, I tested some of these tools. pub

Why Original Writers Matter Despite AI Language Models Creative writers may never be threatened by AI language models.

The crypto market crashed in June 2022. Bitcoin and other cryptocurrencies hit their lowest prices in over a year, causing market panic. Some believe this crash will benefit future investors.

Before I discuss how this crash might help investors, let's examine why it happened. Inflation in the U.S. reached a 30-year high in 2022 after Russia invaded Ukraine. In response, the U.S. Federal Reserve raised interest rates by 0.5%, the most in almost 20 years. This hurts cryptocurrencies like Bitcoin. Higher interest rates make people less likely to invest in volatile assets like crypto, so many investors sold quickly.

The crypto market collapsed. Bitcoin, Ethereum, and Binance dropped 40%. Other cryptos crashed so hard they were delisted from almost every exchange. Bitcoin peaked in April 2022 at $41,000, but after the May interest rate hike, it crashed to $28,000. Bitcoin investors were worried. Even in bad times, this crash is unprecedented.

Bitcoin wasn't "doomed." Before the crash, LUNA was one of the top 5 cryptos by market cap. LUNA was trading around $80 at the start of May 2022, but after the rate hike?

Less than 1 cent. LUNA lost 99.99% of its value in days and was removed from every crypto exchange. Bitcoin's "crash" isn't as devastating when compared to LUNA.

Many people said Bitcoin is "due" for a LUNA-like crash and that the only reason it hasn't crashed is because it's bigger. Still false. If so, Bitcoin should be worth zero by now. We didn't. Instead, Bitcoin reached 28,000, then 29k, 30k, and 31k before falling to 18k. That's not the world's greatest recovery, but it shows Bitcoin's safety.

Bitcoin isn't falling constantly. It fell because of the initial shock of interest rates, but not further. Now, Bitcoin's value is more likely to rise than fall. Bitcoin's low price also attracts investors. They know what prices Bitcoin can reach with enough hype, and they want to capitalize on low prices before it's too late.

Bitcoin's crash was bad, but in a way it wasn't. To understand, consider 2021. In March 2021, Bitcoin surpassed $60k for the first time. Elon Musk's announcement in May that he would no longer support Bitcoin caused a massive crash in the crypto market. In May 2017, Bitcoin's price hit $29,000. Elon Musk's statement isn't worth more than the Fed raising rates. Many expected this big announcement to kill Bitcoin.

Not so. Bitcoin crashed from $58k to $31k in 2021. Bitcoin fell from $41k to $28k in 2022. This crash is smaller. Bitcoin's price held up despite tensions and stress, proving investors still believe in it. What happened after the initial crash in the past?

Bitcoin fell until mid-July. This is also something we’re not seeing today. After a week, Bitcoin began to improve daily. Bitcoin's price rose after mid-July. Bitcoin's price fluctuated throughout the rest of 2021, but it topped $67k in November. Despite no major changes, the peak occurred after the crash. Elon Musk seemed uninterested in crypto and wasn't likely to change his mind soon. What triggered this peak? Nothing, really. What really happened is that people got over the initial statement. They forgot.

Internet users have goldfish-like attention spans. People quickly forgot the crash's cause and were back investing in crypto months later. Despite the market's setbacks, more crypto investors emerged by the end of 2017. Who gained from these peaks? Bitcoin investors who bought low. Bitcoin not only recovered but also doubled its ROI. It was like a movie, and it shows us what to expect from Bitcoin in the coming months.

The current Bitcoin crash isn't as bad as the last one. LUNA is causing market panic. LUNA and Bitcoin are different cryptocurrencies. LUNA crashed because Terra wasn’t able to keep its peg with the USD. Bitcoin is unanchored. It's one of the most decentralized investments available. LUNA's distrust affected crypto prices, including Bitcoin, but it won't last forever.

This is why Bitcoin will likely rebound in the coming months. In 2022, people will get over the rise in interest rates and the crash of LUNA, just as they did with Elon Musk's crypto stance in 2021. When the world moves on to the next big controversy, Bitcoin's price will soar.

Bitcoin may recover for another reason. Like controversy, interest rates fluctuate. The Russian invasion caused this inflation. World markets will stabilize, prices will fall, and interest rates will drop.

Next, lower interest rates could boost Bitcoin's price. Eventually, it will happen. The U.S. economy can't sustain such high interest rates. Investors will put every last dollar into Bitcoin if interest rates fall again.

Bitcoin has proven to be a stable investment. This boosts its investment reputation. Even if Ethereum dethrones Bitcoin as crypto king one day (or any other crypto, for that matter). Bitcoin may stay on top of the crypto ladder for a while. We'll have to wait a few months to see if any of this is true.

This post is a summary. Read the full article here.