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A New Approach to Funding SaaS Companies By Lew Moorman, GP/co-founder Scaleworks During the deployment of our first $60M venture equity fund, we came across so many good software companies that were not a great fit for traditional venture capital. Some were not growing quite fast enough to be viewed as “venture scale”, others wanted to take a more controlled invest as you grow approach. As we studied these situations case-by-case, one thing became very clear: injection of capital could absolutely accelerate their momentum. However, right now there are not many options for companies at that stage — some continue to bootstrap and miss their opportunity, and others raise VC even though they may not really be suitable for that path. some do Unfortunately, traditional banks still require consistent cash flow or tangible assets to collateralize any loan, something that growing SaaS companies typically don’t have. They do, however, have consistent recurring revenue streams, with high gross margins. We believe that is a bankable asset, banks don’t. So we thought…hey, we can provide yet another path for founders and teams with great products. We call it our “venture finance” fund, and we’re ready to deploy $10M immediately. Karl Pichler, GP, Venture Finance The fund will make loans to growing SaaS companies in value of up to 6 months of their recurring revenue. To manage the fund, we’ve brought in Rackspace’s former CFO of 6 years, Karl Pichler as GP, Venture Finance. His experience with financing and advising companies on lowering its cost of capital is second to none. He has also seen the same pushback from banks that we have. Now we get to work on solving these problems together! We’re looking forward to hearing from companies who want to chat about their place in the SaaS world and how we can help scale them from great products to great businesses. Whether we take a majority equity position or help with debt financing, we hope to continue to build a community of SaaS companies building smart and sustainable businesses. Lew and team Scaleworks

Introducing the Scaleworks Venture Finance Fund

Plunging Into Data: Unraveling Time Series Patterns

Depending on who you asked about the outlook of the crypto industry going into 2024, you would have heard a response that ranged between cautious optimism and more of the same from 2023. The prevailing bearish macroeconomic conditions of heightened inflation and elevated interest rates tempered expectations.Even with the potential for a final Bitcoin ETF approval in the U.S. and the next Bitcoin halving, many thought the headwinds were too strong and that the explosion in popularity of AI would siphon off whatever VC money was still available.As we enter Q2 of 2024, most people are shocked at how strong and resilient the industry is proving to be. The popularity of the Bitcoin ETFs with institutional investors and the calming of the Federal Reserve’s language has spurred renewed interest in the highly volatile asset class. But what can we learn from the mistakes of the last bull run, and how can we overcome the reputational damage that crypto scandals did while navigating this new regulatory landscape? How we got here It seems like a distant memory, but 2021 was a banner year for the industry. Most crypto projects were hitting all-time highs, staking and lending protocols like the Anchor Protocoloffered over 20% returns, and charismatic founders like Do Kwon, SBF, and Alex Maschinsky were being hailed by the mainstream media as the next coming of Steve Jobs.Yet, there were warning signs that the market sentiment was about to shift and that these new wunderkinder were nothing more than grifters and charlatans preying on overhyped market sentiment. Even before the bull run in 2021, analysts were highlighting the potential pitfalls of some of the most popular projects of the last cycle. In April 2018,  former Head of Risk at MakerDAO and Research Analyst at Scaler Cyrus Younessi correctly identified the critical failure point in Terra Luna: “If Terra were to fall and break peg, then it would depend on Luna to Save Terra. But Luna would fall as investors would panic, and then Terra would continue to fall, and then they would just each keep contributing to each other’s demise.” Collapses in 2022, bear market 2023 The euphoria that was felt across the market in 2021 would come to an abrupt end in 2022 from the fall of the first of many dominoes with the collapse of Terra Luna. Terra ecosystem In one of the most remarkable collapses of this period, the Terra ecosystem’s flagship protocol, LUNA, and algorithmic stablecoin (UST) lost its peg to the U.S. dollar, causing the price to crash to zero in a matter of days. LUNA’s fall from grace was made all the more spectacular because of how arrogant its founder Do Kwon was about promoting its strength and stability. Celcius Throughout much of 2021, Celsius was considered a premium centralized lending platform. Its charismatic founder, Alex Maschinsky, aggressively promoted its more than 20% return on investment, and it became the de facto custodian for retail and institutional investors. However, the model became unsustainable as the market shifted and the underlying assets began to lose value. It was revealed that Celcius was using Ponzi economics to prop up the platform. It was borrowing new deposits to make up for the lack of funds to pay out the high returns, and the whole platform collapsed under the weight, taking with it investors' deposits. FTX No project was as popular or successful during the 2021 bull run as the centralized exchange FTX. It rose to prominence because of its founder, Sam Bankman Fried’s reputation as a dominant arbitrage trader in his first company, Alameda Research. He was an enigma that the media fawned over as he embraced the lifestyle of a humble billionaire by spouting the benefits of the philosophical lifestyle of effective altruism. FTX became the number one exchange globally, signing massive celebrity endorsements from Larry David, Tom Brady, and Steph Curry. FTX had entrenched itself so deeply into the industry by requiring projects it supported to hold their assets on the platform that when it collapsed, it impacted just about everyone in the space. The once dominant exchange was ultimately undermined by Binance’s founder CZ, after he publicly sold Binance’s entire stake in the FTX utility token FTT after it was revealed SBF had been using customers' deposits to cover Alameda’s losses and using FTT to make up the difference on the balance sheet. Shifting Regulatory Landscape 2022 plunged the crypto industry into a deep bear market that we are only starting to emerge from today. The industry had largely been left to self-regulate, with the SEC only intervening in cases of clear criminality. Yet, the experiences of 2022 left many people heavily criticizing the SEC’s approach, specifically Chairman Gary Gensler and the close relationship he had with SBF and Alameda Research’s CEO Caroline Ellison.In a change of tone, the SEC and Chairman Gensler began pursuing legal action against anyone and everyone it could in an aggressive shift to a regulation policy by enforcement. The shift impacted individual projects like Ripple, Library, and Tornado Cash. The shift also meant constant deferral delays on SEC regulatory decisions, specifically in relation to Bitcoin ETF approvals, as well as targeting good faith actors like centralized exchanges__Kraken__ and Coinbase. Kraken and Coinbase had been asking for clear regulations for years to operate within. For their troubles of trying to remain compliant, the SEC filed lawsuits against each for operating unregistered security exchanges To end 2023, the SEC went trophy hunting and emerged with the arrest of the CEO of the world’s largest crypto exchange (Binance), CZ. CZ pled guilty to anti-money laundering charges and now faces over ten years in prison, while the exchange was fined $ 4 billion. The shift in policy by the SEC represented a direct attack on the industry. Still, it ultimately did more damage by undermining these firms' ability to operate in the U.S. by creating confusion and an environment of ever-shifting regulatory goalposts. Ongoing hacks and heists Despite the SEC's new approach to the industry, heists and hacks throughout the industry remained rampant throughout the course of 2023, totaling over $ 1.7 billion. While 2024 is again proving to be a hotbed for crypto hacks and scams, the first three months totaling over $437 million. With the looming Bitcoin halving and the historical trend of it spurring the next bull run, it is relatively safe to say that this problem will get worse before it gets better. Outlook for 2024 So, with all the negative sentiment built up in the industry from the last cycle. How can we emerge and market the industry in a positive light?If we learned anything from the last cycle it's that the product needs to be centerstage rather than any charismatic leader. It is easy to get swept up in the excitement when the market moves, but we need to remember the fundamentals of DYOR. Accountability for those bad actors of the last cycle is also key. We are beginning to see how the criminal activity of those wunderkind founders was not because they were in crypto but because they were criminals. SBF and Maschinsky committed embezzlement and financial crimes because they took advantage of user funds, not because of anything inherent with the technology. They are more akin to Bernie Madoff than Satoshi Nakamoto. Explaining the difference will be key. We should also take comfort in the fact that despite Chairman Gensler’s disdain for the industry, he will be limited to his term, other chairmen and women in the SEC have dissenting opinions (Hester Pierce) to the SEC, and his legal challenges to reign in the industry often fail. Depending on who you asked about the outlook of the crypto industry going into 2024, you would have heard a response that ranged between cautious optimism and more of the same from 2023. The prevailing bearish macroeconomic conditions of heightened inflation and elevated interest rates tempered expectations. Even with the potential for a final Bitcoin ETF approval in the U.S. and the next Bitcoin halving, many thought the headwinds were too strong and that the explosion in popularity of AI would siphon off whatever VC money was still available. As we enter Q2 of 2024, most people are shocked at how strong and resilient the industry is proving to be. The popularity of the Bitcoin ETFs with institutional investors and the calming of the Federal Reserve’s language has spurred renewed interest in the highly volatile asset class. But what can we learn from the mistakes of the last bull run, and how can we overcome the reputational damage that crypto scandals did while navigating this new regulatory landscape? How we got here It seems like a distant memory, but 2021 was a banner year for the industry. Most crypto projects were hitting all-time highs, staking and lending protocols like the Anchor Protocol offered over 20% returns, and charismatic founders like Do Kwon, SBF, and Alex Maschinsky were being hailed by the mainstream media as the next coming of Steve Jobs. Yet, there were warning signs that the market sentiment was about to shift and that these new wunderkinder were nothing more than grifters and charlatans preying on overhyped market sentiment. Anchor Protocol Anchor Protocol Even before the bull run in 2021, analysts were highlighting the potential pitfalls of some of the most popular projects of the last cycle. In April 2018,  former Head of Risk at MakerDAO and Research Analyst at Scaler Cyrus Younessi correctly identified the critical failure point in Terra Luna: correctly identified correctly identified “If Terra were to fall and break peg, then it would depend on Luna to Save Terra. But Luna would fall as investors would panic, and then Terra would continue to fall, and then they would just each keep contributing to each other’s demise.” “If Terra were to fall and break peg, then it would depend on Luna to Save Terra. But Luna would fall as investors would panic, and then Terra would continue to fall, and then they would just each keep contributing to each other’s demise.” “If Terra were to fall and break peg, then it would depend on Luna to Save Terra. But Luna would fall as investors would panic, and then Terra would continue to fall, and then they would just each keep contributing to each other’s demise.” Collapses in 2022, bear market 2023 The euphoria that was felt across the market in 2021 would come to an abrupt end in 2022 from the fall of the first of many dominoes with the collapse of Terra Luna. Terra ecosystem In one of the most remarkable collapses of this period, the Terra ecosystem’s flagship protocol, LUNA, and algorithmic stablecoin (UST) lost its peg to the U.S. dollar, causing the price to crash to zero in a matter of days. LUNA’s fall from grace was made all the more spectacular because of how arrogant its founder Do Kwon was about promoting its strength and stability. price to crash to zero price to crash to zero Celcius Throughout much of 2021, Celsius was considered a premium centralized lending platform. Its charismatic founder, Alex Maschinsky, aggressively promoted its more than 20% return on investment, and it became the de facto custodian for retail and institutional investors. However, the model became unsustainable as the market shifted and the underlying assets began to lose value. It was revealed that Celcius was using Ponzi economics to prop up the platform. It was borrowing new deposits to make up for the lack of funds to pay out the high returns, and the whole platform collapsed under the weight , taking with it investors' deposits. collapsed under the weight collapsed under the weight FTX No project was as popular or successful during the 2021 bull run as the centralized exchange FTX. It rose to prominence because of its founder, Sam Bankman Fried’s reputation as a dominant arbitrage trader in his first company, Alameda Research. He was an enigma that the media fawned over as he embraced the lifestyle of a humble billionaire by spouting the benefits of the philosophical lifestyle of effective altruism. FTX became the number one exchange globally, signing massive celebrity endorsements from Larry David, Tom Brady, and Steph Curry. FTX had entrenched itself so deeply into the industry by requiring projects it supported to hold their assets on the platform that when it collapsed , it impacted just about everyone in the space. The once dominant exchange was ultimately undermined by Binance’s founder CZ, after he publicly sold Binance’s entire stake in the FTX utility token FTT after it was revealed SBF had been using customers' deposits to cover Alameda’s losses and using FTT to make up the difference on the balance sheet. when it collapsed when it collapsed Shifting Regulatory Landscape 2022 plunged the crypto industry into a deep bear market that we are only starting to emerge from today. The industry had largely been left to self-regulate, with the SEC only intervening in cases of clear criminality. Yet, the experiences of 2022 left many people heavily criticizing the SEC’s approach, specifically Chairman Gary Gensler and the close relationship he had with SBF and Alameda Research’s CEO Caroline Ellison. In a change of tone, the SEC and Chairman Gensler began pursuing legal action against anyone and everyone it could in an aggressive shift to a regulation policy by enforcement. The shift impacted individual projects like Ripple, Library, and Tornado Cash. The shift also meant constant deferral delays on SEC regulatory decisions, specifically in relation to Bitcoin ETF approvals, as well as targeting good faith actors like centralized exchanges__ Kraken __ and Coinbase . Kraken and Coinbase had been asking for clear regulations for years to operate within. For their troubles of trying to remain compliant, the SEC filed lawsuits against each for operating unregistered security exchanges Kraken Coinbase Coinbase filed lawsuits filed lawsuits operating unregistered security exchanges operating unregistered security exchanges To end 2023, the SEC went trophy hunting and emerged with the arrest of the CEO of the world’s largest crypto exchange (Binance), CZ. CZ pled guilty to anti-money laundering charges and now faces over ten years in prison, while the exchange was fined $ 4 billion. The shift in policy by the SEC represented a direct attack on the industry. Still, it ultimately did more damage by undermining these firms' ability to operate in the U.S. by creating confusion and an environment of ever-shifting regulatory goalposts. Ongoing hacks and heists Despite the SEC's new approach to the industry, heists and hacks throughout the industry remained rampant throughout the course of 2023, totaling over $ 1.7 billion. totaling over $ 1.7 billion. totaling over $ 1.7 billion. While 2024 is again proving to be a hotbed for crypto hacks and scams, the first three months totaling over $437 million . With the looming Bitcoin halving and the historical trend of it spurring the next bull run, it is relatively safe to say that this problem will get worse before it gets better. totaling over $437 million totaling over $437 million Outlook for 2024 So, with all the negative sentiment built up in the industry from the last cycle. How can we emerge and market the industry in a positive light? If we learned anything from the last cycle it's that the product needs to be centerstage rather than any charismatic leader. It is easy to get swept up in the excitement when the market moves, but we need to remember the fundamentals of DYOR. Accountability for those bad actors of the last cycle is also key. We are beginning to see how the criminal activity of those wunderkind founders was not because they were in crypto but because they were criminals. SBF and Maschinsky committed embezzlement and financial crimes because they took advantage of user funds, not because of anything inherent with the technology. They are more akin to Bernie Madoff than Satoshi Nakamoto. Explaining the difference will be key. We should also take comfort in the fact that despite Chairman Gensler’s disdain for the industry, he will be limited to his term, other chairmen and women in the SEC have dissenting opinions (Hester Pierce) to the SEC, and his legal challenges to reign in the industry often fail. dissenting opinions dissenting opinions often fail. often fail.

Resilient Strategies for Crypto Marketing Success in 2024: Overcoming Regulations and Scandals

2024 সালে ক্রিপ্টো বিপণন সাফল্যের জন্য স্থিতিস্থাপক কৌশল: প্রবিধান এবং কেলেঙ্কারি কাটিয়ে ওঠা

Belastbare Strategien für erfolgreiches Krypto-Marketing im Jahr 2024: Regulierungen und Skandale überwinden

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One might think that the very first lucky bitcoiner is quite happy these days, but the truth is that, sadly, he left us early. Harold Thomas Finney II, better known as Hal Finney, was a talented American programmer, a remarkable , and the first person to receive Bitcoin ever, from Satoshi Nakamoto himself. He was the first one to run a Bitcoin node and the first miner (besides Satoshi), while greatly helping to spot bugs and exploits in the early days of the cryptocurrency. cypherpunk He was born in California (US) in 1956 and got an engineering bachelor's degree from the California Institute of Technology (CalTech) in 1979. He didn’t always show interest in privacy and cryptography, instead starting his career in games development. He programmed several popular games for Mattel and Atari, Adventures of Tron, Armor Ambush, Astroblast, and Space Attack. including . This was the company that owned and sold the Pretty Good Privacy (PGP) software created by another cypherpunk programmer, Phil Zimmerman. The open version of PGP is now the most widely used email encryption standard. Finney would stay working with them until his forced retirement in 2011, due to his Amyotrophic Lateral Sclerosis (ALS) showing symptoms — the same rare disease the famous astrophysicist Stephen Hawking suffered from. He would later shift to privacy developments, being one of the first hires in the PGP Corporation, founded in 2002 considered Besides this career path, Finney was a dedicated cypherpunk and, along with Eric Hughes, helped to create and run the first anonymous remailer. This is a service that deletes the identifying information in emails. Then, in 2004, he would release another development very close to Bitcoin. Reusable Proof-of-Work (RPOW) In 1997, (another cypherpunk) created one of the first versions of a Proof-of-Work (PoW) system, called Hashcash. It was designed to avoid email spam by offering a small “work token” to every message. Finney was inspired by it to create its own PoW system, called Reusable Proof-of-Work ( ). Adam Back RPOW While both systems utilize Proof of Work tokens, RPOW introduced the notion of creating RSA-signed tokens in exchange for Hashcash tokens. This stands in contrast to Hashcash, where each token could only be used once before becoming invalid. These RPOW tokens could then be transferred between users and exchanged for new RPOWs at each transaction, effectively enabling reuse. In other words, RPOW could be used to create digital currencies, where it served as a method for mitigating spam, preventing denial-of-service attacks, and ensuring the authenticity of transactions. Some years before Bitcoin, RPOW presented itself as one of the first attempts to create a P2P electronic cash system. That’s likely why Finney was so interested in Bitcoin from the very beginning when Nakamoto shared his whitepaper in the cypherpunk mailing list. Bitcoin and Satoshi Nakamoto While other cypherpunks and cryptographers in the list were skeptical at first, Finney as “fascinated” with Bitcoin. So much so that he was among the first individuals to engage with Nakamoto, Finney's active involvement in the early days of Bitcoin's development underscores his contribution to its foundational phase. described himself providing feedback and assistance in debugging the Bitcoin software during its initial stages. He was the first one to run a Bitcoin node, the first miner, and the recipient of the very first Bitcoin transaction —10 BTC from Satoshi Nakamoto. Finney didn’t keep his mining for long though, leaving it “because it made my computer run hot, and the fan noise bothered me.” He expressed a bit of regret for it. However, he was able to recover his wallet when Bitcoin started to show a real market price in 2010. After that, he stayed involved in the cryptocurrency world as a programmer for as long as he could. In 2013, he was already paralyzed due to the sickness and used special software and hardware to communicate and code. In his final coding endeavors, Finney focused on enhancing the security features of Bitcoin wallets by leveraging the capabilities of modern processors designed to support "Trusted Computing." He died in 2014 due to the same disease, and his body is cryopreserved at the Alcor Life Extension Foundation. His wife, Fran, still runs his social media and organizes charity events in his memory. Environmental and Privacy Concerns Despite Finney’s support of Bitcoin, there were certain things that he wanted to change in the system. He commented on Twitter (X) about it during the initial phases of this cryptocurrency. The more energy taken to mine coins the more CO2 released to the atmosphere. He wanted Bitcoin anonymity, and potentially harmful CO2 emissions from Bitcoin mining. to increase also reduce He had the foresight to bring this up in 2009 when nobody thought that Bitcoin would grow enough for this to be a problem. And now it is, indeed. According to the Cambridge Bitcoin Electricity Consumption Index ( ), Bitcoin is taking around 167.7 TWh yearly. That’s more annual energy than countries like Poland, Malaysia, or Norway consume; and 84.9 MtCO2 in annual emissions. Still much less than big industries, but more than entire countries. CBECI produces around The Next Step Sadly, Hal Finney didn’t get to see other new advancements in the crypto industry, but more eco-friendly and also anonymous networks would come over the years. One of them is , which distinguishes itself as an eco-friendly and censorship-proof crypto ecosystem primarily due to its implementation of Directed Acyclic Graph (DAG) technology. Obyte Unlike blockchain-based cryptocurrencies like Bitcoin, which rely on energy-intensive Proof-of-Work (PoW) consensus mechanisms, This architectural choice drastically reduces energy consumption, making Obyte's network far more sustainable and environmentally friendly compared to PoW-based cryptocurrencies. Obyte operates on a DAG ledger structure that eliminates the need for mining. Furthermore, Obyte's ecosystem offers unique features that align with Hal Finney's vision for Bitcoin, particularly in terms of privacy and customization. Through its untraceable cryptocurrency, , users can conduct fully anonymous transactions, fulfilling Finney's interest in cryptographic payment schemes that prioritize privacy. Additionally, Obyte empowers users to , enabling a wide range of applications tailored to specific needs and preferences. Blackbytes create customized private tokens , positioning Obyte as a promising platform that embodies his vision for Bitcoin's evolution. This emphasis on privacy and flexibility mirrors Finney's desire to explore cryptographic solutions that enhance user autonomy and security within the digital economy Read more from series: Cypherpunks Write Code Tim May & Crypto-anarchism Wei Dai & B-money Nick Szabo & Smart Contracts Adam Back & Hashcash Eric Hughes & Remailer St Jude & Community Memory Featured Vector Image by Garry Killian / Freepik

Cypherpunks Write Code: Hal Finney, RPOW, and Bitcoin

সাইফারপাঙ্কস কোড লিখুন: হ্যাল ফিনি, RPOW এবং বিটকয়েন

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🚀🤖✨leveraging Next-Gen RAG Digital Assistants for Enterprise Success 

The future of AI chips is about more than NVIDIA: AMD, Intel, chiplets, upstarts, analog AI, optical computing, and AI chips designed by AI. The interest and investment in AI is skyrocketing, and generative AI is fueling it. Over one-third of CxOs have already embraced GenAI in their operations, with nearly half preparing to invest in it. reportedly What’s powering it all – AI chips – used to receive less attention. Up to the moment, OpenAI’s for a “wildly-ambitious” tech project to boost the world’s chip capacity. Sam Altman claimed he wants to raise up to $7 trillion and sensationalism aside, however, keeping an eye on AI chips means being aware of today’s blockers and tomorrow’s opportunities. Geopolitics According to a , the global AI chip market is expected to reach $89.6 Billion by 2029. The demand for AI chips has increased substantially over time. Growth in AI technology, rising demand for AI chips in consumer electronics, and AI chip innovation all contribute to this forecast. recent study by IMARC Few people have more insights to share on AI hardware than . In an extensive conversation, Pialis shared his insider’s perspective on the AI chip landscape, the transformative rise of chiplets, specialized hardware for training and inference, the future of AI chips and emerging directions like analog and optical computing, and much more. Tony Pialis, CEO and co-founder of Alphawave An AI Leader Behind the Scenes Pialis is a semiconductor serial entrepreneur. He has previously founded and successfully sold two startups: Snowbush Microelectronics and V Semiconductor Inc. V Semiconductor was acquired by Intel in 2012, where Pialis spent a few years as the Vice President of Analog and Mixed-Signal IP. Pialis co-founded AlphaWave in 2017 with the goal of becoming “the next great semiconductor company.” Alphawave went public in 2021 with an impressive market cap of $4.5 billion. The company’s core products include silicon IP, chips, custom silicon, and high-speed interconnect technology tailored for major hyper-scaler customers like Google, Microsoft, Amazon, and Meta. Alphawave is an AI leader behind the scenes, powering hyper scalers and founded on a couple of key premises that Pialis laid out. https://pod.co/orchestrate-all-the-things-podcast-connecting-the-dots-with-george-anadiotis/the-future-of-ai-hardware-leaders-dark-horses-and-rising-stars-featuring-tony-pialis-alphawave-ceo-co-founder?embedable=true , the major players in terms of scaling out data centers and computing these days are no longer the Ciscos of the world. It’s transitioned to Hyperscalers – the Googles, Microsofts, Amazons, and Metas. Hyperscalers have internal design capability, and they also build their own servers, networks, data centers, and campuses. First , the major challenge facing AI moving forward is not computing. Pialis’ view is that the ability to design and implement compute is there. The challenge is the connectivity needed to connect the compute in order to process all of the data. This is what AlphaWave is focusing on. Second The Explosion of Specialized AI Hardware While consumer apps like ChatGPT drove the initial hype cycle in 2023, enterprise adoption reports are mixed. However, according to Pialis, the AI semiconductor sector has seen an enormous explosion of investment and new in the second half of 2023 across industries and geographies. design starts Pialis cited major national initiatives around building domestic AI chip capabilities in the US, Canada, UK, France, Germany, Korea, Taiwan and Japan. After years of relying largely on NVIDIA, governments want to foster their own chip industries to reduce strategic dependence on any one vendor. While NVIDIA CEO Jensen Huang also says that , it does not seem like this also includes the hardware layer. every country needs Sovereign AI Pialis believes this surging demand is spurring not just startups but also tech giants to develop specialized training and inference hardware. In his view, not every organization could or should develop its own AI model, but that’s bound to change. “Over time, AI will inevitably progress to be something like a utility, where hyperscalers will provide access to all of the compute power, like electricity. I think it will be reasonably priced. Anyone will be able to use this utility to train, develop, optimize and deploy their own models. But there’s going to be a lot of profit taking between now and then, and who knows how long it’ll take to eventually reach that state”, said Pialis. Leaders, Dark Horses, and Rising Stars in the AI Chip Race Few people would disagree that NVIDIA is clearly the dominant force in AI accelerators today, and Pialis shares this view. He sees AMD as a strong second place and praised CEO Lisa Su for her leadership. Others such as Ben Lorica also see . favorable winds for AMD in the GenAI chip market But Pialis cautioned not to underestimate Intel, calling it a dark horse that could still prove highly formidable in this market. More specifically, Pialis spoke highly of . Intel’s Habana acqui-hired division led by David Dahan Having worked at Intel, Pialis has an insider view. Having , and kept track of their , we tend to agree. Dahan helped architect new Intel processors that have showcased performance exceeding NVIDIA’s latest GPUs on key benchmarks. covered Habana, met Dahan MLPerf results But performance is not everything. As Pialis also noted, NVIDIA’s software platform including CUDA gives it a huge advantage in the AI chip segment. The ecosystem effects are strong, as engineers and researchers develop optimized frameworks and models for NVIDIA’s architecture. However, there is potential for alternatives; for example, . Pialis opined that a rule of thumb for AI hardware companies is that for every hardware engineer, they need 2-3 software engineers. So even though there’s a lot of talk about NVIDIA and hardware, the vast majority of the investment actually goes into the software. building upon AMD’s hardware and software stack https://www.youtube.com/watch?v=VYx1rGieDmE&embedable=true This was confirmed by NVIDIA’s Dave Salvator. At the , Salvator said that NVIDIA has twice as many software engineers as hardware engineers. That’s not at all by accident, as he emphasized. last MLPerf results briefing for 2023 However, Pialis sees far more potential for challengers in . The reason is that standards are still emerging in that area. As OctoML’s Luis Ceze , there’s for LLM serving, for portable deployment, and and for multiplexed fined-tuned model inference. Inference, is in fact, a bigger market than training as Pialis noted. the inference accelerator market shared vLLM MLC-LLM LoRAX Punica “There’s so much focus on training, large models and the cost of training. But it’s deployment where we all benefit from, on the inference side. That needs significant scale. That needs a different solution. More chips will inevitably be sold on the inference side than the training side. I’m sure business plans also improve with that increase in volume”, Pialis said. Startups like and are attracting significant funding, and so are companies from countries like the UK, Korea, and China trying to reduce reliance on US firms. As far as hyperscalers go, Pialis sees Amazon and Google as being neck to neck, with Microsoft showing strong momentum, and Meta being behind and rumored to acquire one of the smaller startups. Groq Tenstorrent Join the Orchestrate All the Things Newsletter Stories about how Technology, Data, AI, and Media flow into each other shaping our lives. Analysis, Essays, Interviews, and News. Mid-to-long form, 1-3 times per month. The Chiplet Technological Revolution One of the biggest transformations underway in semiconductors, according to Pialis, is the shift to . Previously, progress meant integrating more and more functionality into a single monolithic chip. But transistor dimensions are now down to ~5 atoms wide, and even tiny defects can ruin a whole chip. chiplets Pialis shared an anecdote from his own experience to highlight this. As he related, when he visited OpenAI at some point, he saw a bunch of engineers on their knees praying in front of servers. Not because they were “ ”, but rather because they didn’t want the model they were training to crash. feeling the AGI Some of us may have first heard about chiplets in the context of the China–US trade war that spills over to technology. Chiplets are a key element in the strategy of both and the . China US For Pialis, “chiplets are another technological revolution to deal with the problem that God has thrown at us.” That may sound like a wild appeal to the supernatural, but it has more to do with the challenges of physics at nanoscale. “When we build transistors, which are the basic building blocks of any integrated circuit, we’re stacking atoms. When you’re stacking atoms, the laws of probability, the laws of averaging, fall apart, because now it’s two atoms rather than hundreds of atoms and electrons. So what you get is defects”, Pialis explained. Chiplets are an innovative solution to this nanoscale physics challenge. Rather than one giant chip, designs are split into smaller Lego-like chiplets that can be connected through advanced packaging. This modularity lets chipmakers avoid throwing out entire devices due to one small flaw. The benefits, Pialis said, are important for both makers and buyers. “Silicon is no longer the foundation of leading edge semiconductors; it’s the package. Silicon is now something that is a component on this packaging. There’s been a lot of talk about semiconductor supply chains. There’s a lot of silicon capacity now. Where there’s still very little to no capacity is on the packaging, specifically for designs built using chiplets”, Pialis noted. Chiplets as Lego Building Blocks However, there is a standout example of an AI hardware company that seems to be successful, despite going the opposite way: Cerebras. Cerebras is known for its wafer-scale hardware. Pialis was positive that Cerebras was going to encounter the same defects as everyone else. The difference with Cerebras is they have redundancy. A wafer is like a panel, Pialis noted. Cerebras doesn’t even cut the panel. What they do is they have software that works around the defects. Fundamentally, they’re using silicon as the foundation rather than packaging. They have multiple chips that are arrayed, but they’re not cut. They just connect on the wafer. It definitely is a different approach. However, Pialis added, there are advantages to cutting things up. Imagine a vendor like Intel is trying to build different units such as a CPU, a GPU, a DPU, or a networking device. By breaking things up into smaller pieces, they become like Lego building blocks. So you can have a processor core chiplet, a PCI Express connectivity chiplet, an Ethernet networking chiplet, a DDR memory I/O chiplet, and a memory I/O chiplet. These chiplets can be mixed and matched in a package to build out an entire product portfolio. Pialis thinks that from a design complexity and upfront investment perspective, that’s a winning formula. Pialis sees massive benefits in chiplets, reducing costs by over 60% and power consumption by 40%. This is a major incentive for hyperscale data centers. While he sees Apple, AMD, and Intel leading in chiplets today, Pialis believes chiplets will soon become essential for any company focused on leading-edge hardware. Join the Orchestrate All the Things Newsletter Stories about how Technology, Data, AI, and Media flow into each other shaping our lives.Analysis, Essays, Interviews, and News. Mid-to-long form, 1-3 times per month. Software and Chiplet Modularity, Composability, Programmability Drawing parallels from the world of software engineering, one can wonder how come the modularity that chiplets bring to the table did not prevail earlier. But in hardware, up to now, the winners were the ones that could integrate into a monolithic device the most. The reason is that this drove down cost, which Pialis noted has created “a maniacal focus on integration.” However, as we now approach the dimensions of a single atom, the manufacturing cost eclipses the cost of integration. But even in software, . excessive modularity can also incur excessive overhead Pialis expects that some hardware vendors may take the chiplet approach too far. If functionality is broken into too many tiny pieces, the cost of integrating all of these becomes limiting. So ultimately, it’ll be a hybrid approach that wins. There are two ways of splitting things up with chiplets. The first way of building chiplets is building a single Lego block, mirroring it over and over with the same functionality, and they just talk to each other. That seems more akin to the “traditional” approach. However, one may wonder how these identical chiplet building blocks can be synthesized. The answer is, again, software. Different packages can be composed for different needs, based on the same chiplet, using the software. For example, multiples of 1, 2, 4, or 8 chiplets. The same silicon, just packaged differently, priced differently, and with different software to leverage the incremental compute and memory bandwidth associated with these devices. The other way of building chiplets is by splitting and cutting, creating Lego blocks for different types of functionality. This creates chiplets like a compute Lego block, a training I/O Lego block, a network I/O Lego block, and so on. Pialis sees more momentum behind this approach, as reusable Lego blocks can accelerate other products too. Analog AI, Optical Computing, and AI-Assisted Hardware Design Chiplets may be the immediate way forward. But at a time when power-hungry digital accelerators like GPUs dominate, Pialis claims there’s more to the future of AI chips. And that involves a radical break with the current paradigm. AI needs massively parallelized arithmetic processing, and binary is the dominant approach for this. Using binary compute, numbers are represented as ones and zeros, and floating point arithmetic is done defining precision and range. In analog arithmetic processing, floating point can be represented via voltages or currents. In theory, that may have infinite precision. In the real world where noise exists, that may yield less precision. But that may well work for applications. Using very small currents keeps things operating at low power. Edge AI https://pod.co/orchestrate-all-the-things-podcast-connecting-the-dots-with-george-anadiotis/the-future-of-ai-hardware-leaders-dark-horses-and-rising-stars-featuring-tony-pialis-alphawave-ceo-co-founder?embedable=true There’s another form of compute that some companies are investing in: optical compute for arithmetic. Optical compute uses the properties of optics to implement what are called (multiply & accumulate functionality) which is the core of any arithmetic unit. This results in even lower power demands. MACs Pialis noted that analog and optical approaches are attracting billions in investment to meet specialized needs around computational scale, energy efficiency, and precision. However, it remains unclear if analog techniques can scale effectively to match digital compute for cutting-edge AI models. There is active debate on this question in the hardware community. Even though these techniques are often dismissed as impractical, Pialis believes the investment will eventually pay off. For example, recently, it was reported that capable of processing computer vision tasks with remarkable speed and energy efficiency. researchers at Tsinghua University have developed the world’s first super all-analog photoelectric chip Finally, there’s also the question of . Pialis said that the most effective hardware designers these days are the best software developers. But if AI models can be trained by leveraging the wealth of their experience, that may bring about a sea change. using AI to help design hardware to power AI While the future of AI chips is unknown, Pialis believes the fundamental principles of engineering are timeless. Hopefully, that translates to something that does not end up draining the world’s energy and resources on the promise of future gains to be made.

What Is the Future of AI Chips? Leaders, Dark Horses, and Rising Stars

Multi-Class Classification: Understanding Activation and Loss Functions in Neural Networks

মাল্টি-ক্লাস শ্রেণীবিভাগ: নিউরাল নেটওয়ার্কগুলিতে সক্রিয়করণ এবং ক্ষতি কার্যকারিতা বোঝা

Mehrklassenklassifizierung: Aktivierungs- und Verlustfunktionen in neuronalen Netzen verstehen

Clasificación de clases múltiples: comprensión de las funciones de activación y pérdida en redes neuronales

Classification multiclasse : comprendre les fonctions d'activation et de perte dans les réseaux de neurones

मल्टी-क्लास वर्गीकरण: तंत्रिका नेटवर्क में सक्रियण और हानि कार्यों को समझना

マルチクラス分類: ニューラル ネットワークの活性化関数と損失関数を理解する

다중 클래스 분류: 신경망의 활성화 및 손실 함수 이해

Classificação multiclasse: Compreendendo as funções de ativação e perda em redes neurais

Классификация нескольких классов: понимание функций активации и потерь в нейронных сетях

Çok Sınıflı Sınıflandırma: Sinir Ağlarında Aktivasyon ve Kayıp Fonksiyonlarını Anlamak

Phân loại nhiều lớp: Tìm hiểu các chức năng kích hoạt và mất mát trong mạng thần kinh

多类分类：了解神经网络中的激活函数和损失函数

If we go by , Web Accessibility is the inclusive practice of ensuring there are no barriers that prevent interaction with, or access to, websites on the World Wide Web by people with physical disabilities, situational disabilities, and socio-economic restrictions on bandwidth and speed. When sites are correctly designed, developed and edited, more users have equal access to information and functionality. Wikipedia In simple words, Web Accessibility is the practice of creating such websites that can be used by people, irrespective of any physical disabilities, situational disabilities, or socio-economic restrictions on bandwidth and speed. For example, when adding an image to a html page using tag, passing a meaningful attribute makes it more accessible, as now visually impaired people can also know what that particular image contains using a screen reader. <img> alt The power of the Web is in its universality.Access by everyone regardless of disability is an essential aspect. ~ Tim Berners-Lee, Inventor of the World Wide Web According to an estimate by , 1.3 billion people experience significant disability. This represents 16% of the world’s population, or 1 in 6 of us. World Health Organization The needs that web accessibility aims to address include: Visual Motor/mobility Auditory Seizures Cognitive and Intellectual Understanding Web Accessibility Web was created with the aim of providing informational content to everybody, whatever their background. But when websites or web apps are badly designed, they create barriers and prevent certain sections of the society from accessing the information they provide. Accessibility means creating websites, apps and tools that are accessible to everybody, irrespective of any disability or restrictions. Web Accessibility improves: the way people interact on the web. The contribution of people to the web. Web accessibility encompasses all disabilities that affect access to the Web, including: - Total or significant loss of hearing. Also known as hearing impairment, this condition includes permanent or fluctuating hearing problems. Auditory impairment can range from slight and mild to severe and profound. Auditory - A cognitive impairment (also known as an intellectual disability) is a term used when a person has certain limitations in mental functioning and in skills such as communication, self-help, and social skills. Cognitive - Neurological disorders are diseases of the central and peripheral nervous system. These disorders include epilepsy, Alzheimer disease and other dementias, cerebrovascular diseases including stroke, migraine and other headache disorders, multiple sclerosis, Parkinson's disease, neuro-infections, brain tumors and many others. Neurological - Physical disability indicates any physical limitations or disabilities that inhibit the physical function of one or more limbs of a certain person. It can be temporary or permanent. The causes of this kind of disease are various. Physical - Speech disorders or speech impairments are a type of communication disorder in which normal speech is disrupted. This can mean fluency disorders like stuttering, cluttering or lisps. Someone who is unable to speak due to a speech disorder is considered mute. Speech - Vision impairment occurs when an eye condition affects the visual system and its vision functions. Visual Common Web Accessibility Barriers Let's take a look at some common accessibility barriers that people commonly face: Auditory Barriers People suffering from deafness or any other auditory problem cannot hear voice in videos, podcasts and other sources of audio on the Web. That's why captions and audio transcriptions should be provided to help them understand what is being conveyed by the audio. For individuals with hearing difficulties, the absence of auditory cues presents a substantial obstacle in comprehending information conveyed through spoken words. As a result, providing captions and audio transcriptions becomes imperative to bridge this communication gap. Captions, in the form of synchronized text displayed on-screen, enable individuals with hearing impairments to follow along with the dialogue or narrative of a video. Likewise, audio transcriptions, which are textual representations of spoken content, offer a means for these individuals to access and understand the information presented in podcasts and other audio formats. Cognitive & Learning Barriers As the name suggests, this barrier limits the cognitive abilities of a person and hence prevents the person from learning. Everyone is capable of learning, but students who have cognitive learning barriers are likely to need more time to learn and develop certain skills, and there may be certain things they cannot learn, depending on the severity of their learning barrier (Ohio Coalition for the Education of Children with Disabilities, 2020). It's essential to recognize that while everyone is capable of learning, individuals facing cognitive learning barriers may require additional support and accommodations. The severity of these barriers can vary widely, affecting the pace at which individuals acquire new skills and comprehend information. In some cases, there may be certain limitations on what can be learned, depending on the extent of the cognitive challenge. To overcome these barriers, steps should be taken to make web pages simple and easy to understand. Websites should have clear navigation, and they should have consistent design across all pages. Incorporate multimedia elements judiciously. Ensure that images, videos, and other multimedia content enhance understanding rather than causing confusion. Visual Barriers People who suffer from blindness, partial blindness or any other visual impairment cannot see what is written on the page, nor could they type using a normal keyboard. Addressing visual barriers is essential for creating inclusive environments and ensuring equal access to information and activities for people with visual impairments. Websites that present heavy visual element without any text representation can be challenging to understand for them. Text too small, websites which lacks contrast or is presented in difficult-to-read fonts can be a significant barrier for individuals with visual impairments. Environments that lack audio cues or information can be disorienting for individuals with visual impairments. Addressing visual barriers requires a multifaceted approach, incorporating both physical modifications and the integration of inclusive design principles in digital and informational contexts. Special keyboards called Braille Keyboards are available in the market, which allow visually impaired people to type. Speech Recognition systems should be used on websites, and they should be improved to understand non-English languages more precisely. Images should have captions to make them understandable by screen readers and other assistive technologies. Text should be legible to read and users should be provided with the option to configure text. Motor & Mobility Barriers Technology and devices that are not designed to accommodate individuals with motor impairments, make it challenging for them to interact with computers, smartphones, or other digital interfaces. Solutions like voice recognition, touch screens with customizable controls and alternative input devices like gesture recognition devices should be developed and implemented at lower costs, so that everyone can have access to the Web. Web Content Accessibility Guidelines (WCAG) According to , Web Content Accessibility Guidelines (WCAG) is developed through the W3C process in cooperation with individuals and organizations around the world, with a goal of providing a single shared standard for web content accessibility that meets the needs of individuals, organizations, and governments internationally. World Wide Web Consortium (W3C) WCAG is a technical standard to make web pages accessible. For each guideline, there are testable success criteria. The success criteria are at three levels: - the Web page satisfies all the Level A Success Criteria, or a conforming alternate version is provided. A - the Web page satisfies all the Level A and Level AA Success Criteria, or a Level AA conforming alternate version is provided. AA - the Web page satisfies all the Level A, Level AA and Level AAA Success Criteria, or a Level AAA conforming alternate version is provided. AAA According to , the guidelines and Success Criteria are organized around the following four principles, which lay the foundation necessary for anyone to access and use Web content. Anyone who wants to use the Web must have content that is: W3C - Information and user interface components must be presentable to users in ways they can perceive. This means that users must be able to perceive the information being presented (it can't be invisible to all of their senses) Perceivable - User interface components and navigation must be operable. This means that users must be able to operate the interface (the interface cannot require interaction that a user cannot perform) Operable - Information and the operation of user interface must be understandable. This means that users must be able to understand the information as well as the operation of the user interface (the content or operation cannot be beyond their understanding) Understandable - Content must be robust enough that it can be interpreted reliably by a wide variety of user agents, including assistive technologies. This means that users must be able to access the content as technologies advance (as technologies and user agents evolve, the content should remain accessible) Robust If any of these are not true, users with disabilities will not be able to use the Web. Testing & Tools When you create an accessible website, you must always test it for accessibility to know how accessible it is. Testing helps you to identify any loopholes in accessibility in your website and improve it over time. Now, let's dive into accessibility testing tools. Accessibility Testing Tools There are two types of accessibility testing tools:- - These are automated tools which evaluate a website's code and content for accessibility and provide guidance on how to fix the issues. And, save your time. Automated Testing Tools - These require you to manually scan one page at a time and can cat the errors which automated tools might fail to catch. Manual Testing Tools There are various accessibility testing tools available that can assist web developers, designers, and content creators in identifying potential accessibility issues. Here are some commonly used accessibility testing tools: WAVE (Web Accessibility Evaluation Tool) WAVE is a suite of evaluation tools that help authors make their web content more accessible to individuals with disabilities. It provides visual feedback about the accessibility of your web content by injecting icons and indicators into your page. axe Accessibility Checker The axe Accessibility Checker is a browser extension that can automatically scan your website for WCAG 2.1 conformance and provide detailed information about identified issues. Google Lighthouse Google Lighthouse is an open-source, automated tool for improving the quality of web pages. It has audits for performance, accessibility, progressive web apps, SEO, and more. Siteimprove Accessibility Checker Siteimprove offers an Accessibility Checker that helps you identify and fix accessibility issues on your website. It provides clear explanations of issues and suggested solutions. Pa11y Pa11y is a command-line interface that allows you to run accessibility tests on your web pages. It is flexible and can be integrated into different development workflows. Final Words Web Accessibility is an essential component of building websites and webpages. Yet, many developers (including me) forget this while creating a website. While building a perfectly accessible website is nearly impossible (nothing is perfect), but we should definitely try to create such websites and apps which can be accessed by most sections of the society, if not all. 🎊 New Year, New Commitments: Embrace Web Accessibility! As we joyfully welcome the dawn of a new year, let's make a collective resolution to foster a digital space that celebrates diversity and ensures equal access for all. Make a promise to prioritize web accessibility in all your digital endeavors. Let's commit to creating a more welcoming online environment for everyone. Pledge for Inclusivity: Acknowledge the richness of human experiences and perspectives. Ensure that your online platforms reflect inclusivity, accommodating users with varying abilities and needs. Celebrate Diversity: Kick-start the year by auditing your digital content using accessibility tools. Identify and address potential barriers to create a more universally accessible website. Perform an Accessibility Check: Share the importance of web accessibility with your network. Encourage colleagues, friends, and fellow creators to embark on the journey toward a more inclusive online world. Spread the Message: Just as you set personal and professional goals for the new year, integrate accessibility goals into your projects. Small steps today lead to a more accessible tomorrow. Set Accessible Goals: Let your commitment to accessibility shine brightly in the digital realm. Illuminate the path for others to follow, fostering a community that values and champions inclusivity. Illuminate the Digital Landscape: Cheers to a Year of Inclusion and Empowerment! 🥂 May this new year be a testament to our shared commitment to accessibility, creating a web that transcends barriers and uplifts every user. Here's to a year of progress, understanding, and digital harmony! And lastly... Wishing you a year filled with joy, success, and memorable moments. Happy New Year! Also appears . here

Creating an All Inclusive Web

Ed Maste, the Director of Technology at the FreeBSD Foundation, discusses the rapidly-evolving interplay between hardware, cybersecurity, open source, and instruction set architectures. 1. Cybersecurity is traditionally thought of from a software mindset—but what do developers need to understand about the role of hardware security? Hardware is often the battleground where cyber attackers and security software vie for control. Because of that, there are tremendous opportunities for robust hardware security to effectively pull the rug out from under would-be attacks by simply disallowing attack mechanisms from functioning in the first place.
 
Hardware security already plays a prominent role in several key areas. For example, trusted platform modules (TPMs) and secure boot ensure that devices run only trusted software while booting, eliminating the opportunity for attack software to compromise or tamper with systems. Hardware-based encryption solutions that protect data on hard drives and networks are another common hardware security use case.
 
But more recently, emerging hardware security technologies are introducing some welcome new capability architectures for thwarting ever-more-sophisticated threats that software-based security measures have struggled to address. As these hardware capabilities and architectures grow to reshape mainstream cybersecurity practices, teams should be prepared to adapt and adopt new strategies that incorporate the combined protections of both software and hardware-based measures. 2. How has open source development impacted hardware security? I believe that the active collaboration intrinsic to developing and advancing open source projects—software or hardware—makes for a more hardened solution than just about any closed-source organization could produce. Mature open source projects have so many eyeballs on them and so many diverse, global perspectives that constant iteration is inherent. Hardware security is particularly receptive to these benefits.
 
With open source, anybody interested in contributing to a hardware security project can openly review all hardware designs and source code. That open invitation to test for vulnerabilities and introduce new creative approaches and improvements has allowed hardware security to innovate and evolve at a rapid clip. It’s reasonable to argue that many of the most exciting hardware security technologies available today would not exist or wouldn’t be what they are without the power of open source collaboration. Instruction-set architectures (ISAs) and extensions are a strong example of this, offering open source-hardened hardware foundations from which software can execute securely. 3. Digging deeper into ISAs—what is their role in hardware security? ISAs provide the specification of the instructions that a microporcessor uses to execute commands. Therefore, it’s possible to implement and enforce secure conditions and protections at the foundational level of a device’s operations.
 
ISAs are a critical part of device functionality and hardware security. For example, the x86 ISA is present on most desktops, laptops, and servers (having evolved through the 16-bit, 32-bit, and 64-bit eras). The ARM ISA is a mainstay of smartphones, tablets and other devices including servers (64-bit ARM), due to its low-power consumption attributes. The has thrived as a research platform and is earning increasing attention as a modern flexible option. A major factor in RISC-V’s success is the fact that it’s open source. The open source FreeBSD operating system offers support for x86, ARM, RISC-V, and the OpenPower Foundation’s open source Power ISA. Other ISAs are available as well to provide security and advantageous features for numerous further use cases.
 
When it comes to delivering hardware-level security, specific ISAs offer a variety of valuable features. For instance, an ISA and extensions might provide built-in data execution prevention, data encryption, and memory protection—eliminating the threats of particular attacks or security vulnerabilities within software running on that ISA-protected hardware. RISC-V ISA 4. Are there any shortcomings with ISA, or concerns that developer teams exploring them should be aware of? I think it’s important for teams to understand ISAs beyond just their lists of (compelling) features. They should assemble a logical, comprehensive, and strategic security technology stack that aligns hardware and software protections. Just as ISAs can forbid software-based attacks, they may be incompatible with particular security software. This is especially a concern if the selected ISA isn’t yet a widely supported one.
 
Also, ISAs can suffer from vulnerabilities and attack exploits, similar to how software can be affected. Therefore, organizations must keep their CPU microcode updated to prevent ISA vulnerability risks. 5. What can you tell us about the CHERI ISA, and the FreeBSD-based CheriBSD? A lot! is an open source research project being developed by the University of Cambridge and SRI International, which has gained traction as a hardware security solution via its new approach to data access control and memory protection. With the CHERI ISA, all memory access happens through a “capability” that features hardware-based bounds and permissions. With capabilities controlling access to memory regions and supported by hardware and runtime safeguards, CHERI’s promising spatial memory security offers to neutralize an entire range of memory-focused attacks—such as buffer overflows and use-after-free vulnerabilities. CHERI also shows potential in providing high-performance compartmentalization, such that attackers can’t access data without authorization.
 
While CHERI is churning through its R&D phase, ISA leaders are producing promising prototypes with the technology. is the most advanced of the prototypes; it combines CHERI with a high-performance super-scaler core on a system-on-chip demonstration platform. CheriBSD is a FreeBSD extension enabled with CHERI capabilities, capable of providing memory security and software compartmentalization. Designed for use with Arm’s Morello and CHERI-RISC-V platforms, CheriBSD offers developer teams using those platforms a fully memory-safe environment for desktop software development, and represents a leading achievement in ISA hardware-based security. CHERI Arm’s Morello platform 6. Wrapping up, how should developers feel about hardware security right now? Hardware security is the bedrock of your overall system security. Its role continues to magnify as security threats get more complex. Consider, for example, the growing concern over supply chain attacks, where the bad guys infiltrate the production process of hardware components, embedding backdoors or vulnerabilities that can be exploited later. Incidents like these continue to underscore the need for uber-rigorous hardware security practices—and fortunately there’s a lot of progress to be excited about. I’m also proud of the role FreeBSD is playing in advancing hardware security. Through collaborative efforts and the power of open source communities, the project is an active contributor to the development of secure hardware.

The Cybersecurity Battle Has Come to Hardware

Markus Lanieux Fled a Traffic Stop - He's Now Doing Life in Prison

Markus Lanieux একটি ট্রাফিক স্টপ পালিয়ে গেছে - তিনি এখন কারাগারে জীবন করছেন

Markus Lanieux floh vor einer Verkehrskontrolle – jetzt sitzt er im Gefängnis

Markus Lanieux escapó de una parada de tráfico y ahora cumple cadena perpetua

Markus Lanieux a fui un contrôle routier – il purge désormais une peine de prison à vie

मार्कस लैनियक्स ट्रैफिक स्टॉप से भाग गया - वह अब जेल में जीवन बिता रहा है

マルクス・ラニュー、交通停止から逃走 - 現在は刑務所で暮らしている

Markus Lanieux는 교통 정체를 피해 도망쳤으며 현재 감옥에서 생활하고 있습니다.

Markus Lanieux fugiu de uma parada de trânsito - agora ele está cumprindo prisão perpétua

Маркус Ланье сбежал с остановки и теперь ведет жизнь в тюрьме

Markus Lanieux Trafik Durdurmasından Kaçtı - Şimdi Müebbet Hapis Cezasında

Markus Lanieux Chạy trốn khỏi điểm dừng giao thông - Anh ta hiện đang sống trong tù

马库斯·拉尼厄克斯 (Markus Lanieux) 逃离了交通堵塞 - 他现在在监狱里过着生活

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Shanshan Han & Qifan Zhang, UCI; (2) Wenxuan Wu, Texas A&M University; (3) Baturalp Buyukates, Yuhang Yao & Weizhao Jin, USC; (4) Salman Avestimehr, USC & FedML. Table of Links Abstract and Introduction Problem Setting The Proposed Two-Stages Anomaly Detection Verifiable Anomaly Detection using ZKP Evaluations Related Works Conclusion & References 7 CONCLUSION We present a cutting-edge anomaly detection technique specifically designed for the real-world FL systems. Our approach utilizes an early cross-round check that activates subsequent anomaly detection exclusively in the presence of attacks. When attacks happen, our approach removes anomaly client models efficiently, ensuring that the local models submitted by benign clients remain unaffected. Further, by leveraging ZKPs, participating clients get to verify the integrity of the anomaly detection and removal performed by the server. The practical design and the inherent efficiency of our approach make it well-suited for real-world FL systems. Our plans for future works include extending our approach to asynchronous FL and vertical FL scenarios. REFERENCES T. Aoki. On the stability of the linear transformation in banach spaces. Journal of the Mathematical Society of Japan, 2(1-2):64–66, 1950. E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov. How to backdoor federated learning. In AISTATS, 2020a. Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov. How to backdoor federated learning. In International Conference on Artificial Intelligence and Statistics, pp. 2938–2948. PMLR, 2020b. Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo. Analyzing federated learning through an adversarial lens. In International Conference on Machine Learning, pp. 634– 643. PMLR, 2019. P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer. Machine learning with adversaries: Byzantine tolerant gradient descent. In NeurIPS, 2017. David Byrd and Antigoni Polychroniadou. Differentially private secure multi-party computation for federated learning in financial applications. In Proceedings of the First ACM International Conference on AI in Finance, pp. 1–9, 2020. Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konecnˇ y, H Brendan McMa- ` han, Virginia Smith, and Ameet Talwalkar. Leaf: A benchmark for federated settings. arXiv preprint arXiv:1812.01097, 2018. Xiaoyu Cao and Neil Zhenqiang Gong. Mpaf: Model poisoning attacks to federated learning based on fake clients. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3396–3404, 2022. Xiaoyu Cao and Neil Zhenqiang Gong. Mpaf: Model poisoning attacks to federated learning based on fake clients. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3396–3404, 2022. Xiaoyu Cao, Zaixi Zhang, Jinyuan Jia, and Neil Zhenqiang Gong. Flcert: Provably secure federated learning against poisoning attacks. IEEE Transactions on Information Forensics and Security, 17: 3691–3705, 2022. Mingqing Chen, Rajiv Mathews, Tom Ouyang, and Franc¸oise Beaufays. Federated learning of outof-vocabulary words. arXiv preprint arXiv:1903.10635, 2019. Y. Chen, L. Su, and J. Xu. Distributed statistical machine learning in adversarial settings: Byzantine gradient descent. ACM on Measurement and Analysis of Computing Systems, 1(2):1–25, 2017. Alexander Chowdhury, Hasan Kassem, Nicolas Padoy, Renato Umeton, and Alexandros Karargyris. A review of medical federated learning: Applications in oncology and cancer research. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I, pp. 3–24. Springer, 2022. M. Fang, X. Cao, J. Jia, and N. Gong. Local model poisoning attacks to Byzantine-robust federated learning. In USENIX Security, 2020. B. Feng, L. Qin, Z. Zhang, Y. Ding, and S. Chu. ZEN: An optimizing compiler for verifiable, zero-knowledge neural network inferences. 2021. Cryptology ePrint Archive. Grant S Fletcher. Clinical epidemiology: the essentials. Lippincott Williams & Wilkins, 2019. R. Freivalds. Probabilistic machines can use less running time. In IFIP Congress, 1977. Shuhao Fu, Chulin Xie, Bo Li, and Qifeng Chen. Attack-resistant federated learning with residualbased reweighting. arXiv preprint arXiv:1912.11464, 2019. Clement Fung, Chris JM Yoon, and Ivan Beschastnikh. The limitations of federated learning in sybil settings. In RAID, pp. 301–316, 2020. S. Goldwasser, S. Micali, and C. Rackoff. The knowledge complexity of interactive proof systems. SIAM Jour. on Comp., 18(1):186–208, 1989. J. Groth. On the size of pairing-based non-interactive arguments. In Eurocrypt, 2016. Rachid Guerraoui, Sebastien Rouault, et al. The hidden vulnerability of distributed learning in ´ byzantium. In International Conference on Machine Learning, pp. 3521–3530. PMLR, 2018. S. Han, H. Wang, J. Wan, and J. Li. An iterative scheme for leverage-based approximate aggregation. In IEEE ICDE, 2019. Shanshan Han, Baturalp Buyukates, Zijian Hu, Han Jin, Weizhao Jin, Lichao Sun, Xiaoyang Wang, Chulin Xie, Kai Zhang, Qifan Zhang, et al. Fedmlsecurity: A benchmark for attacks and defenses in federated learning and federated llms. arXiv preprint arXiv:2306.04959, 2023. Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Franc¸oise Beaufays, Sean Augenstein, Hubert Eichner, Chloe Kiddon, and Daniel Ramage. Federated learning for mobile ´ keyboard prediction. arXiv preprint arXiv:1811.03604, 2018. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778, 2016. L. He, S. P. Karimireddy, and M. Jaggi. Byzantine-robust decentralized learning via self-centered clipping. 2022. Available on arXiv:2202.01545. Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, and Tianyi Chen. Cafe: Catastrophic data leakage in vertical federated learning. Advances in Neural Information Processing Systems, 34:994–1006, 2021. Sai Praneeth Karimireddy, Lie He, and Martin Jaggi. Byzantine-robust learning on heterogeneous datasets via bucketing. arXiv preprint arXiv:2006.09365, 2020. Sanjay Kariyappa, Chuan Guo, Kiwan Maeng, Wenjie Xiong, G. Edward Suh, Moinuddin K. Qureshi, and Hsien-Hsin S. Lee. Cocktail party attack: Breaking aggregation-based privacy in federated learning using independent component analysis. In International Conference on Machine Learning, 2022. URL https://api.semanticscholar.org/CorpusID: 252211968. Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay apa Reddi, and Michael Mitzenmacher. Gradient disaggregation: Breaking privacy in federated learning by reconstructing the user participant matrix. In International Conference on Machine Learning, pp. 5959–5968. PMLR, 2021. S. Lee, H. Ko, J. Kim, and H. Oh. vCNN: Verifiable convolutional neural network based on zksnarks. 2020. Cryptology ePrint Archive. David Leroy, Alice Coucke, Thibaut Lavril, Thibault Gisselbrecht, and Joseph Dureau. Federated learning for keyword spotting. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6341–6345, 2019. Suyi Li, Yong Cheng, Wei Wang, Yang Liu, and Tianjian Chen. Learning to detect malicious clients for robust federated learning. arXiv preprint arXiv:2002.00211, 2020. T. Liu, X. Xie, and Y. Zhang. ZkCNN: Zero knowledge proofs for convolutional neural network predictions and accuracy. In ACM CCS, 2021. A. Lyon. Why are normal distributions normal? The British Journal for the Philosophy of Science, 65(3):621–649, 2014. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pp. 1273–1282. PMLR, 2017a. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pp. 1273–1282. PMLR, 2017b. J. Osborne. Improving your data transformations: Applying the Box-Cox transformation. Practical Assessment, Research, and Evaluation, 15(1):12, 2010. Mustafa Safa Ozdayi, Murat Kantarcioglu, and Yulia R Gel. Defending against backdoors in federated learning with robust learning rate. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pp. 9268–9276, 2021. Krishna Pillutla, Sham M Kakade, and Zaid Harchaoui. Robust aggregation for federated learning. IEEE Transactions on Signal Processing, 70:1142–1154, 2022. Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, and Franc¸oise Beaufays. Federated learning for emoji prediction in a mobile keyboard. arXiv preprint arXiv:1906.04329, 2019. M. Rosenblatt. A central limit theorem and a strong mixing condition. National Academy of Sciences, 42(1):43–47, 1956. R. M. Sakia. The Box-Cox transformation technique: A review. Journal of the Royal Statistical Society: Series D, 41(2):169–178, 1992. E. B. Sasson, A. Chiesa, C. Garman, M. Green, I. Miers, E. Tromer, and M. Virza. Zerocash: Decentralized anonymous payments from bitcoin. In IEEE S&P, 2014. Jingwei Sun, Ang Li, Louis DiValentin, Amin Hassanzadeh, Yiran Chen, and Hai Li. Fl-wbc: Enhancing robustness against model poisoning attacks in federated learning from a client perspective. Advances in Neural Information Processing Systems, 34:12613–12624, 2021. Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan. Can you really backdoor federated learning? arXiv preprint arXiv:1911.07963, 2019. Circom Contributors. Circom zkSNARK ecosystem, 2022. https://github.com/iden3/circom. Vale Tolpegin, Stacey Truex, Mehmet Emre Gursoy, and Ling Liu. Data poisoning attacks against federated learning systems. In European Symposium on Research in Computer Security, pp. 480– 501. Springer, 2020. Richard Tomsett, Kevin Chan, and Supriyo Chakraborty. Model poisoning attacks against distributed machine learning systems. In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, volume 11006, pp. 481–489. SPIE, 2019. H. Wang, K. Sreenivasan, S. Rajput, H. Vishwakarma, S. Agarwal, J. Sohn, K. Lee, and D. Papailiopoulos. Attack of the tails: Yes, you really can backdoor federated learning. In NeurIPS, 2020. Jianhua Wang. Pass: Parameters audit-based secure and fair federated learning scheme against free rider. arXiv preprint arXiv:2207.07292, 2022. S. Weisberg. Yeo-Johnson power transformations. 2001. Available at https://www.stat.umn.edu/arc/yjpower.pdf. Cong Xie, Oluwasanmi Koyejo, and Indranil Gupta. SLSGD: Secure and Efficient Distributed Ondevice Machine Learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 213–228. Springer, 2020. H. Yang, X. Zhang, M. Fang, and J. Liu. Byzantine-resilient stochastic gradient descent for distributed learning: A Lipschitz-inspired coordinate-wise median approach. In IEEE CDC, 2019. Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter Bartlett. Byzantine-robust distributed learning: Towards optimal statistical rates. In International Conference on Machine Learning, pp. 5650–5659. PMLR, 2018. Zaixi Zhang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong. Implementation of fldetector. https://github.com/zaixizhang/FLDetector, 2022a. Zaixi Zhang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong. Fldetector: Defending federated learning against model poisoning attacks via detecting malicious clients. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2545–2555, 2022b. Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael W. Mahoney, Joseph Gonzalez, Kannan Ramchandran, and Prateek Mittal. Neurotoxin: Durable backdoors in federated learning. In International Conference on Machine Learning, 2022c. URL https: //api.semanticscholar.org/CorpusID:249889464. A APPENDIX A.1 PROOF OF THE RANGE OF PPV B ZKP IMPLEMENTATION B.1 CHOICE OF THE ZKP SYSTEM In our implementation, we use the Groth16 (Groth, 2016) zkSNARK scheme implemented in the Circom library (Circom Contributors, 2022) for all the computations described above. We chose this ZKP scheme because its construction ensures constant proof size (128 bytes) and constant verification time. Because of this, Groth16 is popular for blockchain application due to small on-chain computation. There are other ZKP schemes based on different construction that can achieve faster prover time (Liu et al., 2021), but the proof size is too big and verification time is not constant, which is a problem if verifier lacks computational power. The construction of a ZKP scheme that is efficient for both prover and verifier is still an open research direction. B.2 SUPPLEMENTARY EXPERIMENTS FOR IMPORTANCE LAYER

Zero-Knowledge-Proof-Based Anomaly Detection: Conclusion & References

Satoshi Nakamoto - everybody who is into crypto knows this name. But who is behind the name? Wikipedia explains it in an easy way: Satoshi Nakamoto is the name used by the presumed pseudonymous person or persons who developed Bitcoin, authored the Bitcoin white paper, and created and deployed Bitcoin's original reference implementation. However, for some reason, nobody has seen this mysterious Satoshi. All we know about him is that in 2007, he started to work on the code. In 2008, the domain Bitcoin.org was registered. And finally, on 31 October 2009, the Bitcoin white paper was published. On January 9, 2009, Satoshi launched the Bitcoin network by defining the genesis block of Bitcoin with the reward of 50 BTC. With it, the father of cryptocurrency was born, and the era of crypto started. Bitcoin The most funny thing is that nobody knows who Satoshi Nakamoto is and whether it is a single person or a group of people. But, as usual, in such cases, there are versions. The most popular personalities that are believed to be Satoshi Nakamoto are the following. Hal Finney Hal Finney (4 May 1956 – 28 August 2014) was a pioneer in cryptography and the person who was fixing bugs and making improvements in the Bitcoin code. He was also a neighbor of Dorian Nakamoto, another candidate for being the famous Bitcoin founder. The writing manner of Hal Finney was the closest to the manner used by the Bitcoin founder. Why is he not Satoshi? There are several facts that confirm this. Satoshi was actively communicating with one of the Mike Hearn, and transacting Bitcoin just at the same time when Hal was participating in a race in Santa Barbara, California. Bitcoin developers, Photographic proofs that it was him running are also available. Then, Hal was diagnosed with Amyotrophic Lateral Sclerosis (ALS) in August 2009, and since then, his health has been deteriorating. In 2010, he could hardly type. Satoshi was pretty active at that time by posting in the forum and writing emails. Another piece of evidence is the fact that when debugging Bitcoin, Hal writes to Satoshi about the issues encountered. Additionally, Hal denied being Satoshi Nakamoto until his death in 2014 due to ALS complications. At the moment, there are no reasons not to believe him. Dorian Nakamoto Dorian Prentice Satoshi Nakamoto, a Japanese living in the USA, California, is another candidate for being the Bitcoin founder. In 2014, Newsweek published that the identity of Satoshi Nakamoto was revealed. Journalist Leah McGrath Goodman went to Dorian’s home to take an interview and claimed later that during the interview, Dorian mentioned, "I was no longer involved in that, and I cannot discuss it. It's been turned over to other people. They are in charge of it now. I no longer have any connection." Later, Dorian denied being the famous and claimed he misunderstood the journalist’s question. In an interview with The Associated Press, he insisted that he had nothing to do with Bitcoin. Moreover, he used to make such mistakes as calling the main crypto Bitcoin instead of Bitcoin and referring to Bitcoin as a company, which is not correct. Also, he said he had never heard of Gavin Andresen, one of the main Bitcoin developers. Bitcoin creator Nick Szabo Nick Szabo is the most viable candidate for being Satoshi Nakamoto. Why? He is a decentralized currency enthusiast, and he created “bit gold,” which was the Bitcoin precursor and the main characteristics of which later became the ones of Bitcoin. A group of forensic scientists from Aston University analyzed the writing styles of Nick Szabo and the Bitcoin founder and came to the conclusion that it is highly likely that Nick is Satoshi Nakamoto. Additionally, the following similarities also make us believe that Nick may be Satoshi. The Bitcoin whitepaper was written using Latex, and Nick also uses Latex for his publications. Elon Musk also believes that, even though Nick may not be the actual Bitcoin founder, he is the one responsible for the development of the majority of Bitcoin ideas. Even though it is clear that Bit Gold was the Bitcoin precursor, Satoshi has never mentioned the project, which looks logical only if Nick was trying to obscure the fact that Bitcoin was a logical development of Bit Gold and he was the Bitcoin founder. Nick Szabo used to backdate his posts about Bit Gold to make it appear that he published them after the Bitcoin launch. All these facts indicate indirectly that Nick may be behind the Bitcoin whitepaper creation, and he was one of the Bitcoin founders. However, it doesn’t mean that he was the only one who did it. Craig Wright Craig Wright is the brightest and the most scandalous candidate for being Satoshi Nakamoto. Here are just some details about his claims. Wright insisted that some details mentioned in the Bitcoin whitepaper were previously mentioned in his blog. It seems, however, that entries in his blog are backdated to make them look as if they were published before the Bitcoin whitepaper went public. Other his claims are also being doubted. However, whether he is going to prove that he is the Bitcoin founder or not, he has already carved his place in the crypto community. Additionally, he is a founder of and a chief science officer at nChain. Bitcoin SV He is involved in several legal cases, including the one about the Bitcoin whitepaper copyright; other cases are about suing those who call him a scammer. In a trial slated to begin in January 2024, he is going to be finally determined as a scammer or as a rightful Bitcoin founder. However, there is one small detail to note. In his emails, Craig misspelled the main cryptocurrency name. Satoshi Nakamoto wrote the name of this cryptocurrency thousands of times on different occasions and never ever used the version by Craig. Just leaving here the link to to compare. Satoshi’s archive And just to have some fun, I'm leaving here a . I’ve enjoyed listening to it. link to a nice song

The Satoshi Mystery Solved: Who Actually is the Father of Bitcoin?

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