(Hebrew: וואלה! תקשורת בע&quot;מ) is an Israeli internet company headquartered in Tel Aviv and is fully owned by The Jerusalem Post. Until 2020, it was fully owned by Bezeq. Walla!&#x27;s web portal provides news, search (powered by Google Search) and e-mail, among other things.

Walla! Communications Ltd - Wikipedia

Read real reviews and see ratings for Walla Walla, WA Movers for free! This list will help you pick the right pro Movers in Walla Walla, WA.

Top 10 Best Movers in Walla Walla, WA | Angi

The Walla Walla Regional Drug Gang Task Force, and the Walla Walla Police Department Career Criminal Aprehension Team

Walla Walla police seize over $100,000 worth of illicit substances in dual raids | KEPR

More than $100,000 worth of fentanyl, methamphetamine and cannabis was recovered by the Walla Walla Regional Drug and Gang Task Force in a search conducted during the arrest of a

Walla Walla man’s arrest leads to recovery of more than $100K in drugs | Courts And Crime | union-bulletin.com

Jonatan Urich, a media advisor to Prime Minister Benjamin Netanyahu, was spotted in recent days at the Prime Minister's office in the Kirya wearing an IDF uniform, even though he wasn't drafted into reserve service.

Netanyahu's media advisor wears IDF uniform despite not being drafted

Major Matan Algarbali, 32, displays extraordinary instinct and resourcefulness, preventing casualties. Harrowing accounts of war resonate as he recounts his experiences during the conflict.

IDF officer foils Hamas sandal-wearing ambush attempt

Israeli media have espoused a dual role following Hamas’ devastating attack on southern Israeli communities and the war that it set off. Beyond their traditional function as journalists, they are

Israeli media, also traumatized by Hamas attack, become communicators of Israel's message

Israel has knocked out communications in the Gaza Strip with bombardment and artillery fire. Explosions from continuous airstrikes overnight Friday and into Saturday lit up the sky over Gaza City

Live updates | Israel knocks out Gaza communications as protesters call for end to the war

Israel has been striking targets in the Gaza Strip with stepped-up bombardment and artillery fire, knocking out communications and creating a near-blackout of information. Early Saturday, the besieged enclave's 2.

Besieged Gaza cut off from the world after heavy Israeli bombardment knocks out communications

Israeli officials are outraged over U.N. Secretary-General Antonio Guterres’ comment that the deadly Hamas attack on southern Israel “did not happen in a vacuum.”

Israel accuses UN chief of justifying terrorism for saying Hamas attack ‘didn’t happen in a vacuum’

Walla! Communications

walla

DISCLAIMER: This story happened in 2021, but I finally found some time and emotional power to make it written. Please enjoy your reading! Hi Ivan! I have some good news and bad news, as they say. It looks like we'll finish our migration to the cloud today. It's been almost a year, and I've finally gathered the courage to write down the whole story, without embellishing or omitting any details. I hope this truth will help readers do better and learn from our mistakes. 7 PM in California is when Europe is still asleep, and our work here is done, allowing us some rest. That wasn't the case on March 10th. Honestly, I barely remember what March looked like after the 10th. And April too. Time seemed to speed up that spring and only slowed down by summer. We migrated to a cloud of ash. The cloud migration project is now complete! On March 10th (00:47 in France, March 9th evening in California), the OVH data center in Strasbourg burned down. Technically two data centers, if you could call them container units separate buildings. Before this, I only knew Strasbourg as a border city in France with a human rights court. That day, I also learned that our servers were there—and that they were burning. At that point, I had these myths in my mind: The fire will be put out soon.


Our product (firewall) will continue working; clients just won't be able to access their accounts.


We have backups; we'll recover everything None of these statements turned out to be entirely true. But to make it interesting and understandable, let's briefly introduce Wallarm: We sell an API and application firewall that functions as an Ingress/Nginx/Envoy module, analyzes traffic, and stores the results (malicious requests) in the cloud. Clients access the cloud to review UX and retrieve alerts via API to PagerDuty, Slack, etc. We have three clouds; the one in Europe that burned at OVH was the oldest infrastructure-wise. Now, let's address the myths one by one Myth #1: The Fire Will Be Put Out Soon The first message said that a fire had started in one of the rooms of SBG2, nothing alarming. But within a couple of hours, it was clear things were bad, and the data center was lost—burned down completely. The fire also spread to the neighboring container, SBG-1, and half of it burned down as well. Here's a tweet from OVH's CEO Octave Klaba: "We have a major incident on SBG2. The fire declared in the building. Firefighters were immediately on the scene but could not control the fire in SBG2. The whole site has been isolated which impacts all services in SGB1-4. We recommend activating your Disaster Recovery Plan." Interestingly, I had lunch with Octave in San Francisco back in the summer of 2019, where he shared their big plans for expansion and going public. I should have given him our branded fire extinguisher then... Myth #2: Our Product Will Continue Working This was almost true, but not for all clients. We sell software that operates within client infrastructure and is independent of our clouds, one of which had burned down in France. However, this isn't entirely accurate because each new pod in the kube, upon startup, registers in the cloud via API. Without this registration, it doesn't start. Although we had tested scenarios where the cloud disconnects during an already running service, startup was not covered. We were in a situation where, after some time, all Kubernetes clients in Europe would have stopped working. More so, their traffic would have halted until our module was removed from the ingress. Our software operates inline. This problem was solved with a placeholder that outputs a JSON stating that all registrations for pods are accepted without checks. Later, we refactored this part to function properly, now the ingress starts even if the cloud is unavailable. Myth #3: We'll Restore Everything from Backups In short, the backups were in the same data center. When we opted for dedicated servers, we wanted them close for better internal connectivity. Or perhaps we just weren't attentive to where our servers were placed. Either way, backups definitely should have been stored separately and in the cloud. And that's what we do now. We had to restore using logs, old and partial backups from people's machines, by decompiling client rules. Let me elaborate on our rules, called LOM (Local Training Set)—yes, we coined that ourselves. These rules don't reach the client in the same form as they are stored in the cloud; they are compiled into something like a decision tree for faster firewall operation. We needed to create a decompiler for these rules, which we did. We asked clients for their compiled LOM files, decompiled them, and uploaded them back to our cloud. We couldn't just restart client training from scratch as the product operates in blocking mode, with settings to prevent false positives and structures for client application APIs. This project was successfully executed with minimal losses, and we were able to lift the rules back up. Client Support Moving forward—I must say, we DID NOT LOSE A SINGLE CLIENT due to this incident. Despite everything initially going against us, we received not only words of support but real assistance in the form of LOM files, as mentioned earlier. Most importantly, we felt genuine understanding and love from our clients, which is hard to quantify or describe. I want to thank everyone again; it was very heartwarming. I wrote to the CEO of Google Cloud, and he replied. We decided to use Google Cloud in Europe to set up a new cloud there. We didn't have a manager in France, only in the USA. Google isn't the easiest company for communication, and they don't process tickets very quickly. So, I decided to reach out directly to the top on a whim and sent an email to Thomas Kurian, CEO of Google Cloud, with the subject "URGENT: GCP Account Manager is missing in the middle crisis." He responded in just 3 minutes and resolved the issue with the manager. Everything had to be done quickly, and, surprisingly, it worked. Memorable Souvenirs and Awards After the incident, we awarded bonuses to all participants (firefighters), which were categorized based on their contributions to the resolution of the consequences and sleepless nights. To add a bit of humor, we also released a series of 'This is fine' meme dogs, each representing a category of "firefighter" who received it. Hard Lessons Learned To be short, just take this piece of advice and share it with your team now. Use it as a checklist; just believe me, it’s mandatory. Don't store backups in the same data center as your data.
Make secondary backups of critical data in the cloud.
Backup your Kubernetes master.
Conduct annual "drills" to simulate which parts of your system will fail under various circumstances, especially if you have many software components in different locations.
Don't be afraid to share accurate and honest status updates with clients; they will help and support you. Conclusions We live in a world where a fire in a data center caused by a single uninterrupted power supply can burn down two buildings. Design, fire suppression, and isolation offer no guarantees. Rely only on yourself and your team. And mitigate risks with trusted providers (hint, we're now tested). Thanks to our clients and our teams: support for handling all communication and nerves, DevOps for their super adequate actions and swift setup of a new European cloud, development for the decompiler of rules and all programmer efforts, detection, everyone I forgot to mention, and of course, Viktor for coordinating the process and the disaster recovery plan that was ready before the fire. I'm out of words to express my gratitude to everyone involved in this project. Thank you! If you're interested, consider joining us at Wallarm; we're hiring globally and can help with relocation/nomad. We're cool. Write to us at jobs@wallarm.com (especially looking for strong technical product managers (TPMs), DevOps, and developers in Go/C). DISCLAIMER: This story happened in 2021, but I finally found some time and emotional power to make it written. Please enjoy your reading! DISCLAIMER: This story happened in 2021, but I finally found some time and emotional power to make it written. Please enjoy your reading! Hi Ivan! I have some good news and bad news, as they say. It looks like we'll finish our migration to the cloud today. Hi Ivan! I have some good news and bad news, as they say. It looks like we'll finish our migration to the cloud today. It's been almost a year, and I've finally gathered the courage to write down the whole story, without embellishing or omitting any details. I hope this truth will help readers do better and learn from our mistakes. 7 PM in California is when Europe is still asleep, and our work here is done, allowing us some rest. That wasn't the case on March 10th. Honestly, I barely remember what March looked like after the 10th. And April too. Time seemed to speed up that spring and only slowed down by summer. We migrated to a cloud of ash. The cloud migration project is now complete! We migrated to a cloud of ash. The cloud migration project is now complete! On March 10th (00:47 in France, March 9th evening in California), the OVH data center in Strasbourg burned down. Technically two data centers, if you could call them container units separate buildings. Before this, I only knew Strasbourg as a border city in France with a human rights court. That day, I also learned that our servers were there—and that they were burning. At that point, I had these myths in my mind: The fire will be put out soon. Our product (firewall) will continue working; clients just won't be able to access their accounts. We have backups; we'll recover everything The fire will be put out soon. The fire will be put out soon. The fire will be put out soon. Our product (firewall) will continue working; clients just won't be able to access their accounts. Our product (firewall) will continue working; clients just won't be able to access their accounts. Our product (firewall) will continue working; clients just won't be able to access their accounts. We have backups; we'll recover everything We have backups; we'll recover everything We have backups; we'll recover everything None of these statements turned out to be entirely true. But to make it interesting and understandable, let's briefly introduce Wallarm: We sell an API and application firewall that functions as an Ingress/Nginx/Envoy module, analyzes traffic, and stores the results (malicious requests) in the cloud. We sell an API and application firewall that functions as an Ingress/Nginx/Envoy module, analyzes traffic, and stores the results (malicious requests) in the cloud. Clients access the cloud to review UX and retrieve alerts via API to PagerDuty, Slack, etc. Clients access the cloud to review UX and retrieve alerts via API to PagerDuty, Slack, etc. We have three clouds; the one in Europe that burned at OVH was the oldest infrastructure-wise. We have three clouds; the one in Europe that burned at OVH was the oldest infrastructure-wise. Now, let's address the myths one by one Myth #1: The Fire Will Be Put Out Soon Myth #1: The Fire Will Be Put Out Soon The first message said that a fire had started in one of the rooms of SBG2, nothing alarming. But within a couple of hours, it was clear things were bad, and the data center was lost—burned down completely. The fire also spread to the neighboring container, SBG-1, and half of it burned down as well. Here's a tweet from OVH's CEO Octave Klaba: "We have a major incident on SBG2. The fire declared in the building. Firefighters were immediately on the scene but could not control the fire in SBG2. The whole site has been isolated which impacts all services in SGB1-4. We recommend activating your Disaster Recovery Plan." "We have a major incident on SBG2. The fire declared in the building. Firefighters were immediately on the scene but could not control the fire in SBG2. The whole site has been isolated which impacts all services in SGB1-4. We recommend activating your Disaster Recovery Plan." Interestingly, I had lunch with Octave in San Francisco back in the summer of 2019, where he shared their big plans for expansion and going public. I should have given him our branded fire extinguisher then... Myth #2: Our Product Will Continue Working Myth #2: Our Product Will Continue Working This was almost true, but not for all clients. We sell software that operates within client infrastructure and is independent of our clouds, one of which had burned down in France. However, this isn't entirely accurate because each new pod in the kube, upon startup, registers in the cloud via API. Without this registration, it doesn't start. Although we had tested scenarios where the cloud disconnects during an already running service, startup was not covered. We were in a situation where, after some time, all Kubernetes clients in Europe would have stopped working. More so, their traffic would have halted until our module was removed from the ingress. Our software operates inline. This problem was solved with a placeholder that outputs a JSON stating that all registrations for pods are accepted without checks. Later, we refactored this part to function properly, now the ingress starts even if the cloud is unavailable. Myth #3: We'll Restore Everything from Backups Myth #3: We'll Restore Everything from Backups In short, the backups were in the same data center. When we opted for dedicated servers, we wanted them close for better internal connectivity. Or perhaps we just weren't attentive to where our servers were placed. Either way, backups definitely should have been stored separately and in the cloud. And that's what we do now. We had to restore using logs, old and partial backups from people's machines, by decompiling client rules. Let me elaborate on our rules, called LOM (Local Training Set)—yes, we coined that ourselves. These rules don't reach the client in the same form as they are stored in the cloud; they are compiled into something like a decision tree for faster firewall operation. We needed to create a decompiler for these rules, which we did. We asked clients for their compiled LOM files, decompiled them, and uploaded them back to our cloud. We couldn't just restart client training from scratch as the product operates in blocking mode, with settings to prevent false positives and structures for client application APIs. This project was successfully executed with minimal losses, and we were able to lift the rules back up. Client Support Client Support Moving forward—I must say, we DID NOT LOSE A SINGLE CLIENT due to this incident. Despite everything initially going against us, we received not only words of support but real assistance in the form of LOM files, as mentioned earlier. Most importantly, we felt genuine understanding and love from our clients, which is hard to quantify or describe. I want to thank everyone again; it was very heartwarming. I wrote to the CEO of Google Cloud, and he replied. We decided to use Google Cloud in Europe to set up a new cloud there. We didn't have a manager in France, only in the USA. Google isn't the easiest company for communication, and they don't process tickets very quickly. So, I decided to reach out directly to the top on a whim and sent an email to Thomas Kurian, CEO of Google Cloud, with the subject "URGENT: GCP Account Manager is missing in the middle crisis." He responded in just 3 minutes and resolved the issue with the manager. Everything had to be done quickly, and, surprisingly, it worked. Memorable Souvenirs and Awards Memorable Souvenirs and Awards After the incident, we awarded bonuses to all participants (firefighters), which were categorized based on their contributions to the resolution of the consequences and sleepless nights. To add a bit of humor, we also released a series of 'This is fine' meme dogs, each representing a category of "firefighter" who received it. Hard Lessons Learned Hard Lessons Learned To be short, just take this piece of advice and share it with your team now. Use it as a checklist; just believe me, it’s mandatory. Don't store backups in the same data center as your data. Make secondary backups of critical data in the cloud. Backup your Kubernetes master. Conduct annual "drills" to simulate which parts of your system will fail under various circumstances, especially if you have many software components in different locations. Don't be afraid to share accurate and honest status updates with clients; they will help and support you. Don't store backups in the same data center as your data. Don't store backups in the same data center as your data. Make secondary backups of critical data in the cloud. Make secondary backups of critical data in the cloud. Backup your Kubernetes master. Backup your Kubernetes master. Conduct annual "drills" to simulate which parts of your system will fail under various circumstances, especially if you have many software components in different locations. Conduct annual "drills" to simulate which parts of your system will fail under various circumstances, especially if you have many software components in different locations. Don't be afraid to share accurate and honest status updates with clients; they will help and support you. Don't be afraid to share accurate and honest status updates with clients; they will help and support you. Conclusions Conclusions We live in a world where a fire in a data center caused by a single uninterrupted power supply can burn down two buildings. Design, fire suppression, and isolation offer no guarantees. Rely only on yourself and your team. And mitigate risks with trusted providers (hint, we're now tested). Thanks to our clients and our teams: support for handling all communication and nerves, DevOps for their super adequate actions and swift setup of a new European cloud, development for the decompiler of rules and all programmer efforts, detection, everyone I forgot to mention, and of course, Viktor for coordinating the process and the disaster recovery plan that was ready before the fire. I'm out of words to express my gratitude to everyone involved in this project. Thank you! If you're interested, consider joining us at Wallarm; we're hiring globally and can help with relocation/nomad. We're cool. Write to us at jobs@wallarm.com (especially looking for strong technical product managers (TPMs), DevOps, and developers in Go/C). jobs@wallarm.com

How We Migrated to the Cloud... of Ashes (Wallarm OVH Recovery in 2021)

Brian Wallace is the Founder and President of NowSourcing , an industry leading infographic design agency based in Louisville, KY and Cincinnati, OH which works with companies that range from startups to Fortune 500s. Brian has been named a Google Small Business Advisor for 2016-present and joined the SXSW Advisory Board in 2019. In his session with Hacker Noon community Brian has shared his views on infographics, shared useful advice for startups, explained how to build great LinkedIn profile and much more! Ask Me Anything 1.	Do great infographics need animation? "I firmly believe that rich media is a great attention grabber as well as a method to keep the visitor attracted to the content. That said, much of the web is arcane and held together by bubble gum and scotch tape , so we are purposeful in making a lot of flat, 2-dimensional infographics for this reason that we can’t count on standards being supported.
" 2.	What's the Number One mistake startups and small business make when starting up
? "Most businesses fail before they start and don’t even know it. A lot of people are subject matter experts - good at a specific skill - but don’t belong in biz for themselves. If you don’t know how to sell and how to market, you better find a partner, hire someone good at these, or go back to work for someone else." 3.	On the importance of education for career in tech or tech business. "Even though I have extensive formal education, I feel that unless you are getting a terminal degree (lawyer, doctor, HVAC tech, etc), there isn’t as much of a need as there was for this formal career trajectory as in former years. Many companies have come out on record saying that degrees don’t matter. Also there are lots of certificate programs and microcredentialing programs these days to help you.
" 4. 3 Top Tips for LinkedIn profile optimization. "Here’s a 3 step flywheel to ensure that you’re successful on LinkedIn (which btw is incredibly hard to do well): 1 - Make an outstanding LinkedIn profile 2 - Make it super clear what you do for a living 3 - Make regular (doesn’t have to be daily, but maybe 1-3x a week), original, great content - including video" Read the full AMA. VOTE in the 2020 NOONIES before voting closes on 12 October!

Brian Wallace AMA on Infographics, Startups and LinkedIn Optimization

The 2020 #Noonies are here, and they are both much greener and much bigger than last year. Among the 2,000+ deserving humans nominated across 5 categories for over 200 award titles, we discovered , who’s has been nominated for a 2020 #Noonie in the . Brian Wallace from United States Future Heroes and Technology categories Without further ado, we present to you, our big techy world, from the perspective of Brian. 1. Which 2020 Noonie/s have you been nominated for? Founder of the Year 2. Tell us a bit about yourself. Brian Wallace is the Founder and President of NowSourcing, an industry leading infographic design agency based in Louisville, KY and Cincinnati, OH which works with companies that range from startups to Fortune 500s. Brian also runs #LinkedInLocal events nationwide, and hosts the Next Action Podcast.  Brian has been named a Google Small Business Advisor for 2016-present and joined the SXSW Advisory Board in 2019. 3. Tell us about the things you make / write / manage / build. I'm involved with marketing campaigns that really challenge people to think deeper beyond the surface for many different industries. I also enjoy writing here on Hackernoon as well as a number of specialized trade publications. Since the COVID-19 pandemic, I've taken a keen interest in virtual summits, having a hand in speaking and organizing many of them. 4. What are you most excited about right now? The future of virtual summits and events as people likely won't feel comfortable in large crowds and mass travel for some time to come. 5. What are you worried about right now? The irrationality in the world right now which could lead to very poor decision making. 6. What's the most useful advice you've ever given somebody? Specialization and focus is key right now.  A clear and rational mind is the new currency in a world gone mad. 7. How has the pandemic changed your life and/or career? For the better, overall. Business has been good but I do worry about my friends. I moved from the coast (NYC area) before the housing market crisis and have been in the south/midwest for the better part of a decade.  Greatly enjoy the better quality of live / housing here and since many are embracing remote work and such alternatives, I can do most of what I need to do without traveling to larger areas and conferences now. 8. If we gave you $10 million to invest in one thing right now, where would you put it? Cracking the issue of conferences and summits. it's a 1.5 trillion dollar opportunity that's up for grabs right now and nobody is adequately addressing it. 9. What's an opinion you have that most people don't agree with? That the human mind is pretty easily manipulated and people are running around with strongly held opinions that they don't know the basis for.  Ask me about how breakfast is the most important meal of the day. 10. Which apps can't you live without? Slack, WhatsApp, Google Apps, and LinkedIn 11. What are you currently learning? Parenting skills.  We are checking out how to be foster/respite care parents. For much the same reasons Hacker Noon decided not to put good ideas behind pop-ups or paywalls — nor abuse your personal data to target you with creepy ads — we also decided that you don’t have to be a #thinkfluencer or have 50k followers on Twitter to earn the recognition that comes with a 2020 Noonie Nomination. Make somebody’s day and nominate them to be recognized in the internet’s most independent and community-driven awards: . NOONIES.TECH

"A Clear And Rational Mind Is The New Currency" - Brian Wallace, 2020 Noonie Nominee

Imagine discovering an investment that goes up 50% on average each year with occasional big drops that you can buy for even further gains. Would you buy it? No. What if that asset went up 10% on average each year? Would you buy it? Yes. Chances are, you already have. I’m talking about Bitcoin. You pay a high price for a cheery consensus That may sound weird. Stupid, even. Who would turn down a 50% opportunity in exchange for a 10% opportunity? The US stock market only gives you 7% annualized. I agree. Yet, that’s what people do when they buy Bitcoin during bull markets like today’s. They don’t buy Bitcoin for $16,000, $32,000, $48,000, or $64,000. Instead, they buy it for $80,000 or more. They heard some people got ridiculously rich with Bitcoin and think they can do it, too. Two years ago, maybe. That was during the depths of a wretched crypto bear market. Now? One Bitcoin is worth more than the average US worker’s annual salary. Bitcoin’s market cap is almost $2 trillion. Most people can’t afford to buy enough Bitcoin to get rich, and those who already have Bitcoin don’t have enough upside to see the massive multiples necessary to retire their bloodlines. A letdown, if you want it to be You won’t find such realistic expectations on social media or the email solicitations you get from other crypto gurus. Some tell you “banana zone will send prices to the moon” and “S2F” predicts a $500,000 bitcoin in 2025. Others admit the chance to get rich with Bitcoin has passed, but tell you you need to buy smaller cryptos with bloated market caps and massive selling from insiders. Nobody will pay $2,500 for a newsletter subscription or spend 50 hours reading investment blogs for a 50% annualized return, even though that beats most traders and every professional fund manager. They’ll gladly pay as much or more for five altcoin picks that will make you $5 million or a secret strategy to make a quick fortune. Only one outcome is realistic, but you pay more for the fantasy. Cryptocurrency is a marathon masquerading as a sprint. They sell you on the sprint, but the real gains come from the marathon. Your best bet This is not what you're supposed to believe. Consensus opinion told you to buy above $50,000 in 2021, sell at $40,000 in 2022, and buy again at $32,000 in 2023. With that approach, a $50,000 investment would have grown to $75,000 by July 2024. That’s a 50% return in three years! Way better than the S&P 500's 16% return over that time. At today’s price of $90,000, you’re at 60% gains. Boom! You still come out way ahead of the S&P 500’s 25% return to date. Feels great, right? Your best financial advisors can barely beat the stock market. You crushed them. You gained 60% in three years! Fun fact: If you had bought Bitcoin blindly, on a fixed schedule, in a fixed amount, regardless of circumstances, you would have bought at an average price of roughly $40,000 for a 123% gain. My plan beat both approaches for a return of 160% over that time. You bought at an average price of roughly $34,000. If you sold when I told you to, you did even better. https://cryptoiseasy.beehiiv.com/p/plan-buying-selling-bitcoin?embedable=true Maybe not rich but pretty, pretty, pretty good While 160% is better than 123%, it's not the huge, life-changing returns you have in your head. You might get that with altcoins, but with 50,000 to choose from, you're going to have to get lucky and get your timing perfect. Mark, past performance doesn’t dictate future results! True, but it’s the only way we can set realistic expectations. Can you get rich with Bitcoin by doubling your money every few years? Yes, if you start with a decent amount of money, stick around for years, buy the big crashes and bear markets, and buy a lot when you buy. In other words, you need time, money, and patience. Most likely, you're getting into crypto now because you want to make money quickly, with as little effort as possible and don't have much capital to invest. No time, money, or patience. The good news? You can beat 50% annualized returns with Bitcoin. How? Wait for better opportunities. Subscribe to my newsletter, Crypto is Easy, so you know when those opportunities come. You'll also get occasional trading opportunities and altcoin reports, as well as access to my plan and everything else you get with your subscription. Timing is everything With Bitcoin, you may not end up with fabulous riches, a garage full of Lambo's, and enough money to build a nuclear bunker, but you'll do better than all of your friends. Wall Street is on board. Trump’s a fan. Bitcoin’s network continues to grow. If you’re patient, you will have tons of upside. It's all about planning properly. That may seem disappointing. It's not what your favorite guru tells you in their email solicitations. But honestly, is it so bad to wait for a happy, easy way to buy into an asset class that has outpaced every other investment of the past 10 years and offers unlimited growth in the future? Mark Helfman publishes the Crypto is Easy newsletter. He is also the author of three books and a top Bitcoin writer on Medium and Hacker Noon. Learn more about him in his bio and connect with him on Tealfeed. Imagine discovering an investment that goes up 50% on average each year with occasional big drops that you can buy for even further gains. Would you buy it? No. What if that asset went up 10% on average each year? Would you buy it? Yes. Chances are, you already have. I’m talking about Bitcoin. You pay a high price for a cheery consensus That may sound weird. Stupid, even. Who would turn down a 50% opportunity in exchange for a 10% opportunity? The US stock market only gives you 7% annualized. I agree. Yet, that’s what people do when they buy Bitcoin during bull markets like today’s. They don’t buy Bitcoin for $16,000, $32,000, $48,000, or $64,000. Instead, they buy it for $80,000 or more. They heard some people got ridiculously rich with Bitcoin and think they can do it, too. Two years ago, maybe. That was during the depths of a wretched crypto bear market. Now? One Bitcoin is worth more than the average US worker’s annual salary. Bitcoin’s market cap is almost $2 trillion. Most people can’t afford to buy enough Bitcoin to get rich, and those who already have Bitcoin don’t have enough upside to see the massive multiples necessary to retire their bloodlines. A letdown, if you want it to be You won’t find such realistic expectations on social media or the email solicitations you get from other crypto gurus. Some tell you “banana zone will send prices to the moon” and “S2F” predicts a $500,000 bitcoin in 2025. Others admit the chance to get rich with Bitcoin has passed, but tell you you need to buy smaller cryptos with bloated market caps and massive selling from insiders. Nobody will pay $2,500 for a newsletter subscription or spend 50 hours reading investment blogs for a 50% annualized return, even though that beats most traders and every professional fund manager. They’ll gladly pay as much or more for five altcoin picks that will make you $5 million or a secret strategy to make a quick fortune. Only one outcome is realistic, but you pay more for the fantasy. Cryptocurrency is a marathon masquerading as a sprint. They sell you on the sprint, but the real gains come from the marathon. Your best bet This is not what you're supposed to believe. Consensus opinion told you to buy above $50,000 in 2021, sell at $40,000 in 2022, and buy again at $32,000 in 2023. With that approach, a $50,000 investment would have grown to $75,000 by July 2024. That’s a 50% return in three years! Way better than the S&P 500's 16% return over that time. At today’s price of $90,000, you’re at 60% gains. Boom! You still come out way ahead of the S&P 500’s 25% return to date. Feels great, right? Your best financial advisors can barely beat the stock market. You crushed them. You gained 60% in three years! You gained 60% in three years! You gained 60% in three years! Fun fact: If you had bought Bitcoin blindly, on a fixed schedule, in a fixed amount, regardless of circumstances, you would have bought at an average price of roughly $40,000 for a 123% gain . 123% gain My plan beat both approaches for a return of 160% over that time. You bought at an average price of roughly $34,000. If you sold when I told you to, you did even better. My plan My plan a return of 160% https://cryptoiseasy.beehiiv.com/p/plan-buying-selling-bitcoin?embedable=true https://cryptoiseasy.beehiiv.com/p/plan-buying-selling-bitcoin?embedable=true Maybe not rich but pretty, pretty, pretty good While 160% is better than 123%, it's not the huge, life-changing returns you have in your head. You might get that with altcoins, but with 50,000 to choose from, you're going to have to get lucky and get your timing perfect. Mark, past performance doesn’t dictate future results! Mark, past performance doesn’t dictate future results! True, but it’s the only way we can set realistic expectations. Can you get rich with Bitcoin by doubling your money every few years? Yes, if you start with a decent amount of money, stick around for years, buy the big crashes and bear markets, and buy a lot when you buy. In other words, you need time, money, and patience. Most likely, you're getting into crypto now because you want to make money quickly, with as little effort as possible and don't have much capital to invest. No time, money, or patience. The good news? You can beat 50% annualized returns with Bitcoin. How? Wait for better opportunities. Subscribe to my newsletter, Crypto is Easy , so you know when those opportunities come. Crypto is Easy Crypto is Easy Crypto is Easy You'll also get occasional trading opportunities and altcoin reports, as well as access to my plan and everything else you get with your subscription . my plan my plan everything else you get with your subscription Timing is everything With Bitcoin, you may not end up with fabulous riches, a garage full of Lambo's, and enough money to build a nuclear bunker, but you'll do better than all of your friends. Wall Street is on board. Trump’s a fan. Bitcoin’s network continues to grow. If you’re patient, you will have tons of upside. It's all about planning properly. That may seem disappointing. It's not what your favorite guru tells you in their email solicitations. But honestly, is it so bad to wait for a happy, easy way to buy into an asset class that has outpaced every other investment of the past 10 years and offers unlimited growth in the future? Mark Helfman publishes the Crypto is Easy newsletter. He is also the author of three books and a top Bitcoin writer on Medium and Hacker Noon . Learn more about him in his bio and connect with him on Tealfeed . Crypto is Easy Crypto is Easy Crypto is Easy Medium Medium Medium Hacker Noon Hacker Noon Hacker Noon bio bio Tealfeed Tealfeed

Stop Thinking You’ll Get Rich With Bitcoin

OFF Radio Krakow, a radio station in Poland, has made a groundbreaking move and replaced its human presenters with AI. And this is just the beginning. In the not-too-distant future, many radio and television stations will likely begin to replace their presenters with AI due to advancements in natural language processing and video synthesis technologies. AI technology can also help translate scripts and interviews from one language to another. Imagine CNN, MSNBC, or FOX using AI instead of human staff. According to a BBC report, “In Kuwait, an AI persona by the name of Fedha ran through the headlines for Kuwait News. Hermes presented the news in May 2023 for Greek state broadcaster ERT. South Korean broadcaster SBS handed over the duties of news presenting to Zae-In, an AI-generated deepfake, for five months this year. There are others in India and Taiwan, too – all created by AI.” It may not be a bad idea. For one, this could potentially reduce mistakes, gaffes, and unpleasant moments. It could also save costs. According to The Wrap, Anderson Cooper is said to earn an estimated $20 million a year; Wolf Blitzer, $15 million; Jake Tapper, $8.5 million; and Chris Wallace, who makes about $8 million. Other networks no doubt pay their hosts similar salaries, and it is no wonder that companies are showing a keen interest in understanding how AI could work for them and whether the move is worthwhile. So far, the idea seems to suggest it would pay off financially. In terms of programming, AI hosts can literally provide news 24 hours a day without getting tired. They can also deliver breaking news within seconds, don’t need make-up, and don’t go on vacation. While these are, of course, great advantages for networks seeking to cut costs and operate continuously, they don’t solve every problem. Networks will maintain the role of human broadcasters in fields that require deep contextual knowledge, personal experience, or emotional intelligence, like investigative journalism and complex live reporting. There are other reasons as well. While in much of the world, journalists view the use of artificial intelligence as a looming threat to livelihoods, in Venezuela – where showing your face on a news report can conceivably land you in jail – many people view it as protection. In an interesting development in this field, Microsoft and OpenAI are investing up to $10 million to help local news organizations implement artificial intelligence in their operations. Each recipient outlet will receive funding to hire a two-year AI expert, along with $5 million worth of combined cash and technology credits split between Microsoft Azure and OpenAI services. The program aims to help local news organizations develop new revenue streams and improve their operations using AI tools. The program is part of the Lenfest Institute's Local Independent News Coalition, which includes eight of the largest independently owned metropolitan news organizations in the United States. These outlets will work together to develop AI solutions that uphold journalistic standards while strengthening their business models. According to Open Society Foundations, AI and Large Language Models (LLMs), in particular, “are likely to bring about significant and lasting structural change to information ecosystems as we know them.” Global concerns about the use of AI in news production and misinformation are growing, a report published by the Reuters Institute for the Study of Journalism found. The report found that consumers are suspicious about the use of AI to create news content, particularly for sensitive subjects such as politics. According to the survey, 52% of U.S. respondents and 63% of UK respondents said they would be uncomfortable with news produced mostly with AI. The report surveyed 2,000 people in each country, noting that respondents were more comfortable with behind-the-scenes uses of AI to make journalists' work more efficient. However, this specific poll deals with journalists using AI to write stories as opposed to newsrooms using AI characters to deliver the news on television. Part of the problem is that AI avatars cannot interpret or express humor or deal with complex ethical judgments. Certainly in a round- table setting, AI will not be able to engage in banter and various degrees of analysis. For this type of journalism, perhaps the most important kind where the public relies on journalists to decipher and analyze important news, AI will remain insufficient, and the need for human presenters will continue. OFF Radio Krakow, a radio station in Poland, has made a groundbreaking move and replaced its human presenters with AI . replaced its human presenters with AI And this is just the beginning. In the not-too-distant future, many radio and television stations will likely begin to replace their presenters with AI due to advancements in natural language processing and video synthesis technologies. AI technology can also help translate scripts and interviews from one language to another. Imagine CNN, MSNBC, or FOX using AI instead of human staff. According to a BBC report , “In Kuwait, an AI persona by the name of Fedha ran through the headlines for Kuwait News. Hermes presented the news in May 2023 for Greek state broadcaster ERT. South Korean broadcaster SBS handed over the duties of news presenting to Zae-In, an AI-generated deepfake, for five months this year. There are others in India and Taiwan, too – all created by AI.” BBC report Hermes presented the news handed over the duties It may not be a bad idea. For one, this could potentially reduce mistakes, gaffes, and unpleasant moments. It could also save costs. According to The Wrap , Anderson Cooper is said to earn an estimated $20 million a year; Wolf Blitzer, $15 million; Jake Tapper, $8.5 million; and Chris Wallace, who makes about $8 million. According to The Wrap Other networks no doubt pay their hosts similar salaries, and it is no wonder that companies are showing a keen interest in understanding how AI could work for them and whether the move is worthwhile. So far, the idea seems to suggest it would pay off financially. In terms of programming, AI hosts can literally provide news 24 hours a day without getting tired. They can also deliver breaking news within seconds, don’t need make-up, and don’t go on vacation. While these are, of course, great advantages for networks seeking to cut costs and operate continuously, they don’t solve every problem. Networks will maintain the role of human broadcasters in fields that require deep contextual knowledge, personal experience, or emotional intelligence, like investigative journalism and complex live reporting. There are other reasons as well. While in much of the world, journalists view the use of artificial intelligence as a looming threat to livelihoods, in Venezuela – where showing your face on a news report can conceivably land you in jail – many people view it as protection. can conceivably land you in jail In an interesting development in this field, Microsoft and OpenAI are investing up to $10 million to help local news organizations implement artificial intelligence in their operations. to help local news organizations implement artificial intelligence Each recipient outlet will receive funding to hire a two-year AI expert, along with $5 million worth of combined cash and technology credits split between Microsoft Azure and OpenAI services. The program aims to help local news organizations develop new revenue streams and improve their operations using AI tools. The program is part of the Lenfest Institute's Local Independent News Coalition, which includes eight of the largest independently owned metropolitan news organizations in the United States. These outlets will work together to develop AI solutions that uphold journalistic standards while strengthening their business models. According to Open Society Foundations , AI and Large Language Models (LLMs), in particular, “are likely to bring about significant and lasting structural change to information ecosystems as we know them.” Open Society Foundations Global concerns about the use of AI in news production and misinformation are growing, a report published by the Reuters Institute for the Study of Journalism found. The report found that consumers are suspicious about the use of AI to create news content, particularly for sensitive subjects such as politics. According to the survey, 52% of U.S. respondents and 63% of UK respondents said they would be uncomfortable with news produced mostly with AI. The report surveyed 2,000 people in each country, noting that respondents were more comfortable with behind-the-scenes uses of AI to make journalists' work more efficient. However, this specific poll deals with journalists using AI to write stories as opposed to newsrooms using AI characters to deliver the news on television. Part of the problem is that AI avatars cannot interpret or express humor or deal with complex ethical judgments. Certainly in a round- table setting, AI will not be able to engage in banter and various degrees of analysis. For this type of journalism, perhaps the most important kind where the public relies on journalists to decipher and analyze important news, AI will remain insufficient, and the need for human presenters will continue.

AI Presenters Won’t Take Over TV and Radio - Yet

Why Laziness is a Superpower

Чаму лянота - гэта звышздольнасць

Por qué la pereza es un superpoder

Miksi laiskuus on supervoima

Zašto je lijenost supermoć

Perché la pigrizia è un superpotere

למה עצלות היא מעצמת על

なぜ怠惰はスーパーパワーなのか

Kāpēc slinkums ir lielvara

Залхуурал яагаад супер хүч вэ?

Ke ka baka la’ng Botswa e le Matla a Magolo

Imaraykutaq Qella kayqa Superpoder

Waa maxay sababta caajisnimadu u tahay awood sare

Authors:
(1) Divyanshu Kumar, Enkrypt AI;
(2) Anurakt Kumar, Enkrypt AI;
(3) Sahil Agarwa, Enkrypt AI;
(4) Prashanth Harshangi, Enkrypt AI. Table of Links Abstract and 1 Introduction 2 Problem Formulation and Experiments 3 Experiment Set-up & Results 4 Conclusion and References A. Appendix 4 CONCLUSION Our work investigates the LLM’s safety against Jailbreak attempts. We have demonstrated how fine-tuned and quantized models are vulnerable to jailbreak attempts and stress the importance of using external guardrails to reduce this risk. Fine-tuning or quantizing model weights alters the risk profile of LLMs, potentially undermining the safety alignment established through RLHF. This could result from catastrophic forgetting, where LLMs lose memory of safety protocols, or the finetuning process shifting the model’s focus to new topics at the expense of existing safety measures. The lack of safety measures in these fine-tuned and quantized models is concerning, highlighting the need to incorporate safety protocols during the fine-tuning process. We propose using these tests as part of a CI/CD stress test before deploying the model. The effectiveness of guardrails in preventing jailbreaking highlights the importance of integrating them with safety practices in AI development. This approach not only enhances AI models but also establishes a new standard for responsible AI development. By ensuring that AI advancements prioritize innovation and safety, we promote ethical AI deployment, safeguarding against potential misuse and fostering a secure digital future. REFERENCES Zifan Wang Andy Zou. AdvBench Dataset, July 2023. URL https://github.com/ llm-attacks/llm-attacks/tree/main/data. Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, and Eric Wong. Jailbreaking Black Box Large Language Models in Twenty Queries. arXiv, October 2023. doi: 10.48550/arXiv.2310.08419. Edoardo Debenedetti, Giorgio Severi, Nicholas Carlini, Christopher A. Choquette-Choo, Matthew Jagielski, Milad Nasr, Eric Wallace, and Florian Tramer. Privacy Side Channels in Machine ` Learning Systems. arXiv, September 2023. doi: 10.48550/arXiv.2309.05610. Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. QLoRA: Efficient Finetuning of Quantized LLMs. arXiv, May 2023. doi: 10.48550/arXiv.2305.14314. Micah Gorsline, James Smith, and Cory Merkel. On the Adversarial Robustness of Quantized Neural Networks. arXiv, May 2021. doi: 10.1145/3453688.3461755. Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. Not what you’ve signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection. arXiv, February 2023. doi: 10.48550/arXiv.2302.12173. Bing He, Mustaque Ahamad, and Srijan Kumar. PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Models. In KDD ’21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 575–584. Association for Computing Machinery, New York, NY, USA, August 2021. ISBN 978-1-45038332-5. doi: 10.1145/3447548.3467390. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-Rank Adaptation of Large Language Models. arXiv, June 2021. doi: 10.48550/arXiv.2106.09685. Shuhei Kashiwamura, Ayaka Sakata, and Masaaki Imaizumi. Effect of Weight Quantization on Learning Models by Typical Case Analysis. arXiv, January 2024. doi: 10.48550/arXiv.2401. 17269. Siwon Kim, Sangdoo Yun, Hwaran Lee, Martin Gubri, Sungroh Yoon, and Seong Joon Oh. ProPILE: Probing Privacy Leakage in Large Language Models. arXiv, July 2023. doi: 10.48550/arXiv. 2307.01881. Aounon Kumar, Chirag Agarwal, Suraj Srinivas, Aaron Jiaxun Li, Soheil Feizi, and Himabindu Lakkaraju. Certifying LLM Safety against Adversarial Prompting. arXiv, September 2023. doi: 10.48550/arXiv.2309.02705. Thai Le, Suhang Wang, and Dongwon Lee. MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models. IEEE Computer Society, November 2020. ISBN 978-1- 7281-8316-9. doi: 10.1109/ICDM50108.2020.00037. Yi Liu, Gelei Deng, Zhengzi Xu, Yuekang Li, Yaowen Zheng, Ying Zhang, Lida Zhao, Tianwei Zhang, and Yang Liu. Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study. arXiv, May 2023. doi: 10.48550/arXiv.2305.13860. Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson, Hyrum Anderson, Yaron Singer, and Amin Karbasi. Tree of Attacks: Jailbreaking Black-Box LLMs Automatically. arXiv, December 2023. doi: 10.48550/arXiv.2312.02119. Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. Training language models to follow instructions with human feedback. arXiv, March 2022. doi: 10.48550/arXiv.2203.02155. Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson. Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! arXiv, October 2023. doi: 10.48550/arXiv.2310.03693. Traian Rebedea, Razvan Dinu, Makesh Narsimhan Sreedhar, Christopher Parisien, and Jonathan Cohen. NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails. ACL Anthology, pp. 431–445, December 2023. doi: 10.18653/v1/2023. emnlp-demo.40. Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. Jailbroken: How Does LLM Safety Training Fail? arXiv, July 2023. doi: 10.48550/arXiv.2307.02483. Martin Weyssow, Xin Zhou, Kisub Kim, David Lo, and Houari Sahraoui. Exploring ParameterEfficient Fine-Tuning Techniques for Code Generation with Large Language Models. arXiv, August 2023. doi: 10.48550/arXiv.2308.10462. Yisong Xiao, Aishan Liu, Tianyuan Zhang, Haotong Qin, Jinyang Guo, and Xianglong Liu. RobustMQ: Benchmarking Robustness of Quantized Models. arXiv, August 2023. doi: 10.48550/ arXiv.2308.02350. Andy Zhou, Bo Li, and Haohan Wang. Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks. arXiv, January 2024. doi: 10.48550/arXiv.2401.17263. Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, Ani Nenkova, and Tong Sun. AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models. arXiv, October 2023. doi: 10.48550/arXiv.2310.15140. Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, and Matt Fredrikson. Universal and Transferable Adversarial Attacks on Aligned Language Models. arXiv, July 2023. doi: 10.48550/arXiv.2307.15043. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Divyanshu Kumar, Enkrypt AI; (2) Anurakt Kumar, Enkrypt AI; (3) Sahil Agarwa, Enkrypt AI; (4) Prashanth Harshangi, Enkrypt AI. Authors: Authors: (1) Divyanshu Kumar, Enkrypt AI; (2) Anurakt Kumar, Enkrypt AI; (3) Sahil Agarwa, Enkrypt AI; (4) Prashanth Harshangi, Enkrypt AI. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Problem Formulation and Experiments 2 Problem Formulation and Experiments 3 Experiment Set-up & Results 3 Experiment Set-up & Results 4 Conclusion and References 4 Conclusion and References A. Appendix A. Appendix 4 CONCLUSION Our work investigates the LLM’s safety against Jailbreak attempts. We have demonstrated how fine-tuned and quantized models are vulnerable to jailbreak attempts and stress the importance of using external guardrails to reduce this risk. Fine-tuning or quantizing model weights alters the risk profile of LLMs, potentially undermining the safety alignment established through RLHF. This could result from catastrophic forgetting, where LLMs lose memory of safety protocols, or the finetuning process shifting the model’s focus to new topics at the expense of existing safety measures. The lack of safety measures in these fine-tuned and quantized models is concerning, highlighting the need to incorporate safety protocols during the fine-tuning process. We propose using these tests as part of a CI/CD stress test before deploying the model. The effectiveness of guardrails in preventing jailbreaking highlights the importance of integrating them with safety practices in AI development. This approach not only enhances AI models but also establishes a new standard for responsible AI development. By ensuring that AI advancements prioritize innovation and safety, we promote ethical AI deployment, safeguarding against potential misuse and fostering a secure digital future. REFERENCES Zifan Wang Andy Zou. AdvBench Dataset, July 2023. URL https://github.com/ llm-attacks/llm-attacks/tree/main/data. Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, and Eric Wong. Jailbreaking Black Box Large Language Models in Twenty Queries. arXiv, October 2023. doi: 10.48550/arXiv.2310.08419. Edoardo Debenedetti, Giorgio Severi, Nicholas Carlini, Christopher A. Choquette-Choo, Matthew Jagielski, Milad Nasr, Eric Wallace, and Florian Tramer. Privacy Side Channels in Machine ` Learning Systems. arXiv, September 2023. doi: 10.48550/arXiv.2309.05610. Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. QLoRA: Efficient Finetuning of Quantized LLMs. arXiv, May 2023. doi: 10.48550/arXiv.2305.14314. Micah Gorsline, James Smith, and Cory Merkel. On the Adversarial Robustness of Quantized Neural Networks. arXiv, May 2021. doi: 10.1145/3453688.3461755. Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. Not what you’ve signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection. arXiv, February 2023. doi: 10.48550/arXiv.2302.12173. Bing He, Mustaque Ahamad, and Srijan Kumar. PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Models. In KDD ’21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 575–584. Association for Computing Machinery, New York, NY, USA, August 2021. ISBN 978-1-45038332-5. doi: 10.1145/3447548.3467390. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-Rank Adaptation of Large Language Models. arXiv, June 2021. doi: 10.48550/arXiv.2106.09685. Shuhei Kashiwamura, Ayaka Sakata, and Masaaki Imaizumi. Effect of Weight Quantization on Learning Models by Typical Case Analysis. arXiv, January 2024. doi: 10.48550/arXiv.2401. 17269. Siwon Kim, Sangdoo Yun, Hwaran Lee, Martin Gubri, Sungroh Yoon, and Seong Joon Oh. ProPILE: Probing Privacy Leakage in Large Language Models. arXiv, July 2023. doi: 10.48550/arXiv. 2307.01881. Aounon Kumar, Chirag Agarwal, Suraj Srinivas, Aaron Jiaxun Li, Soheil Feizi, and Himabindu Lakkaraju. Certifying LLM Safety against Adversarial Prompting. arXiv, September 2023. doi: 10.48550/arXiv.2309.02705. Thai Le, Suhang Wang, and Dongwon Lee. MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models. IEEE Computer Society, November 2020. ISBN 978-1- 7281-8316-9. doi: 10.1109/ICDM50108.2020.00037. Yi Liu, Gelei Deng, Zhengzi Xu, Yuekang Li, Yaowen Zheng, Ying Zhang, Lida Zhao, Tianwei Zhang, and Yang Liu. Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study. arXiv, May 2023. doi: 10.48550/arXiv.2305.13860. Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson, Hyrum Anderson, Yaron Singer, and Amin Karbasi. Tree of Attacks: Jailbreaking Black-Box LLMs Automatically. arXiv, December 2023. doi: 10.48550/arXiv.2312.02119. Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. Training language models to follow instructions with human feedback. arXiv, March 2022. doi: 10.48550/arXiv.2203.02155. Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson. Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! arXiv, October 2023. doi: 10.48550/arXiv.2310.03693. Traian Rebedea, Razvan Dinu, Makesh Narsimhan Sreedhar, Christopher Parisien, and Jonathan Cohen. NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails. ACL Anthology, pp. 431–445, December 2023. doi: 10.18653/v1/2023. emnlp-demo.40. Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. Jailbroken: How Does LLM Safety Training Fail? arXiv, July 2023. doi: 10.48550/arXiv.2307.02483. Martin Weyssow, Xin Zhou, Kisub Kim, David Lo, and Houari Sahraoui. Exploring ParameterEfficient Fine-Tuning Techniques for Code Generation with Large Language Models. arXiv, August 2023. doi: 10.48550/arXiv.2308.10462. Yisong Xiao, Aishan Liu, Tianyuan Zhang, Haotong Qin, Jinyang Guo, and Xianglong Liu. RobustMQ: Benchmarking Robustness of Quantized Models. arXiv, August 2023. doi: 10.48550/ arXiv.2308.02350. Andy Zhou, Bo Li, and Haohan Wang. Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks. arXiv, January 2024. doi: 10.48550/arXiv.2401.17263. Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, Ani Nenkova, and Tong Sun. AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models. arXiv, October 2023. doi: 10.48550/arXiv.2310.15140. Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, and Matt Fredrikson. Universal and Transferable Adversarial Attacks on Aligned Language Models. arXiv, July 2023. doi: 10.48550/arXiv.2307.15043. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Stories, News & Mentions about WALLA

Walla’s Stories on HackerNoon

Walla's News & Mentions