Birla Opus: The account was won after an intense multi agency pitch and will be handled by the Leo Burnett Mumbai team.

Birla Opus appoints Leo Burnett India as creative and strategic agency - ET BrandEquity

Best Media Info: As the brand’s communication partners Leo Burnett India will craft the creative strategy and positioning for the overall brand ‘Birla Opus’, ‘Birla Opus One’ and ‘Birla Opus Calista’, th| Ad Craft News

Aditya Birla Group onboards Leo Burnett as AoR for to design brand architecture of Birla Opus: Best Media Info

CLSA is also anticipating a de-rating to take place for Asian Paints and a move back to its 15-year average multiple. As a result, it has cut its financial year 2025 - 2026 earnings estimates by 8% and 10% respectively.

Asian Paints gets a downgrade, lowest price target post Birla Opus launch

Birla Opus, from the House of Aditya Birla Group, has appointed DDB Mudra as creative partner for four brands in the range, following a multi-agency pitch. The mandate calls for crafting strategy and communications for Birla Opus which offers a wide array of decorative paints aimed at businesses ...

Birla Opus Taps DDB Mudra as Creative Partner | Branding in Asia

Opus

opus

PALO ALTO, USA/California, September 11th, 2024/CyberNewsWire/--Opus’ innovative engine integrates AI-driven intelligence, contextual data and automated decision-making to drive precise, efficient vulnerability remediation. Opus Security, the leader in unified cloud-native remediation, today announced the launch of its Advanced Multi-Layered Prioritization Engine, designed to revolutionize how organizations manage, prioritize and remediate security vuvulnerabilities. Leveraging AI-driven intelligence, deep contextual data and automated decision-making capabilities, this innovative engine helps organizations prioritize the most critical vulnerabilities, enhancing both security posture and operational efficiency. A Breakthrough in Vulnerability Remediation Security teams are overwhelmed by the need to rapidly prioritize alerts from multiple tools across various attack surfaces. These may include redundant alerts or negligible findings and teams must decide which to address first without adequate information, context and ability to do so. Security teams struggle to identify and address the most critical issues, and developers have limited time and scope to devote to security fixes—especially when it isn’t clear what is important and what is negligible. Developers are often bombarded with alerts that are duplicates or irrelevant due to inefficient prioritization—wasting time and increasing friction and frustration. Opus Security’s Advanced Multi-Layered Prioritization Engine is a transformative approach to vulnerability management. By integrating multiple layers of intelligence, contextual analysis and risk mitigation, the engine ensures that security teams can effectively prioritize and address the most critical vulnerabilities, reducing risk, enhancing operational efficiency and supporting overall business goals. The engine integrates traditional vulnerability severity scoring with dynamic exploitability analysis, detailed environmental context and an automated decision-making process to provide a robust method for ranking vulnerabilities. A key component of this engine is the AI-Based Vulnerability Intelligence Layer, which goes beyond traditional severity scoring. This layer leverages over 700 real-time threat intelligence feeds to build a deep and nuanced understanding of each vulnerability’s risk. By incorporating intelligence from sources such as dark web forums, social media, open-source tools, exploit databases and active threat campaigns, the engine can flag high-risk issues with unparalleled accuracy. This intelligence-driven approach ensures that organizations are aware of vulnerabilities and their likelihood of exploitation in the wild, allowing for proactive and informed remediation efforts. Using a five-layered framework, the engine first performs a Base Severity Assessment, aggregating severity scores from leading security tools and public databases to ensure that no critical vulnerabilities are overlooked. Next, the AI-Based Vulnerability Intelligence layer leverages real-time threat intelligence to flag high-risk issues based on their likelihood of exploitation. The Contextual Impact layer then prioritizes vulnerabilities according to their relevance to specific business functions, protecting critical systems first, especially those that handle sensitive data. The engine is the first to enable real SSVC decision-making, fully baked into the product. This helps teams categorize vulnerabilities into specific response actions based on the affected environment's severity, exploitability and criticality. Finally, the Risk Customization layer allows organizations to tailor prioritization according to their unique risk appetite and operational needs. Additionally, Opus Security introduces Effortless Data Querying, allowing users to interact with the platform using natural language. This feature enables users to quickly refine vulnerability lists based on specific concerns and make precise, data-driven decisions by leveraging advanced AI-powered insights. Driving Value and Operational Excellence The engine’s multi-layered approach ensures unprecedented precision in risk management by integrating real-time intelligence with detailed contextual analysis. This integration enables SSVC decision-making, allowing security teams to focus on vulnerabilities that truly matter, reducing the likelihood of overlooking critical vulnerabilities. Opus aligns security decisions with business priorities by deeply understanding the organization's structure, critical services and risk profiles, driving context-aware decision-making that protects critical assets and directly supports strategic goals. “Opus’ new Advanced Multi-Layered Prioritization Engine is a game-changer in vulnerability remediation, simplifying, streamlining and optimizing the process considerably. The engine's ability to prioritize the vulnerabilities that pose the greatest risk reduces overall security costs and helps security and developer teams avoid unnecessary remediation of low-risk issues,” said Meny Har, CEO of Opus Security. “Minimizing friction between development and security teams, driving smoother collaboration and ensuring that security measures do not impede the development process means that all teams can focus on what matters and fix what counts.” About Opus Security Opus Security is at the forefront of cloud-native vulnerability remediation, delivering solutions that streamline remediation across complex IT ecosystems. Opus Security provides unparalleled visibility and control over vulnerabilities by integrating existing security tools and enhancing them with advanced AI and contextual intelligence. The platform’s innovative features, including the new Advanced Multi-Layered Prioritization Engine, empower organizations to protect their most critical assets with confidence and precision. For more information about Opus Security and its solutions, visit: https://www.opus.security/. Contact Senior Account Executive Hannah Sather Montner Tech PR hsather@montner.com This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. PALO ALTO, USA/California, September 11th, 2024/CyberNewsWire/--Opus’ innovative engine integrates AI-driven intelligence, contextual data and automated decision-making to drive precise, efficient vulnerability remediation. Opus Security, the leader in unified cloud-native remediation, today announced the launch of its Advanced Multi-Layered Prioritization Engine, designed to revolutionize how organizations manage, prioritize and remediate security vuvulnerabilities. Leveraging AI-driven intelligence, deep contextual data and automated decision-making capabilities, this innovative engine helps organizations prioritize the most critical vulnerabilities, enhancing both security posture and operational efficiency. A Breakthrough in Vulnerability Remediation Security teams are overwhelmed by the need to rapidly prioritize alerts from multiple tools across various attack surfaces. These may include redundant alerts or negligible findings and teams must decide which to address first without adequate information, context and ability to do so. Security teams struggle to identify and address the most critical issues, and developers have limited time and scope to devote to security fixes—especially when it isn’t clear what is important and what is negligible. Developers are often bombarded with alerts that are duplicates or irrelevant due to inefficient prioritization—wasting time and increasing friction and frustration. Opus Security’s Advanced Multi-Layered Prioritization Engine is a transformative approach to vulnerability management. By integrating multiple layers of intelligence, contextual analysis and risk mitigation, the engine ensures that security teams can effectively prioritize and address the most critical vulnerabilities, reducing risk, enhancing operational efficiency and supporting overall business goals. The engine integrates traditional vulnerability severity scoring with dynamic exploitability analysis, detailed environmental context and an automated decision-making process to provide a robust method for ranking vulnerabilities. A key component of this engine is the AI-Based Vulnerability Intelligence Layer, which goes beyond traditional severity scoring. This layer leverages over 700 real-time threat intelligence feeds to build a deep and nuanced understanding of each vulnerability’s risk. By incorporating intelligence from sources such as dark web forums, social media, open-source tools, exploit databases and active threat campaigns, the engine can flag high-risk issues with unparalleled accuracy. This intelligence-driven approach ensures that organizations are aware of vulnerabilities and their likelihood of exploitation in the wild, allowing for proactive and informed remediation efforts. Using a five-layered framework, the engine first performs a Base Severity Assessment, aggregating severity scores from leading security tools and public databases to ensure that no critical vulnerabilities are overlooked. Next, the AI-Based Vulnerability Intelligence layer leverages real-time threat intelligence to flag high-risk issues based on their likelihood of exploitation. The Contextual Impact layer then prioritizes vulnerabilities according to their relevance to specific business functions, protecting critical systems first, especially those that handle sensitive data. The engine is the first to enable real SSVC decision-making, fully baked into the product. This helps teams categorize vulnerabilities into specific response actions based on the affected environment's severity, exploitability and criticality. Finally, the Risk Customization layer allows organizations to tailor prioritization according to their unique risk appetite and operational needs. Additionally, Opus Security introduces Effortless Data Querying, allowing users to interact with the platform using natural language. This feature enables users to quickly refine vulnerability lists based on specific concerns and make precise, data-driven decisions by leveraging advanced AI-powered insights. Driving Value and Operational Excellence The engine’s multi-layered approach ensures unprecedented precision in risk management by integrating real-time intelligence with detailed contextual analysis. This integration enables SSVC decision-making, allowing security teams to focus on vulnerabilities that truly matter, reducing the likelihood of overlooking critical vulnerabilities. Opus aligns security decisions with business priorities by deeply understanding the organization's structure, critical services and risk profiles, driving context-aware decision-making that protects critical assets and directly supports strategic goals. “Opus’ new Advanced Multi-Layered Prioritization Engine is a game-changer in vulnerability remediation, simplifying, streamlining and optimizing the process considerably. The engine's ability to prioritize the vulnerabilities that pose the greatest risk reduces overall security costs and helps security and developer teams avoid unnecessary remediation of low-risk issues,” said Meny Har, CEO of Opus Security. “Opus’ new Advanced Multi-Layered Prioritization Engine is a game-changer in vulnerability remediation, simplifying, streamlining and optimizing the process considerably. The engine's ability to prioritize the vulnerabilities that pose the greatest risk reduces overall security costs and helps security and developer teams avoid unnecessary remediation of low-risk issues,” said Meny Har, CEO of Opus Security. “Minimizing friction between development and security teams, driving smoother collaboration and ensuring that security measures do not impede the development process means that all teams can focus on what matters and fix what counts.” “Minimizing friction between development and security teams, driving smoother collaboration and ensuring that security measures do not impede the development process means that all teams can focus on what matters and fix what counts.” About Opus Security Opus Security is at the forefront of cloud-native vulnerability remediation, delivering solutions that streamline remediation across complex IT ecosystems. Opus Security provides unparalleled visibility and control over vulnerabilities by integrating existing security tools and enhancing them with advanced AI and contextual intelligence. The platform’s innovative features, including the new Advanced Multi-Layered Prioritization Engine, empower organizations to protect their most critical assets with confidence and precision. For more information about Opus Security and its solutions, visit: https://www.opus.security/. Contact Senior Account Executive Hannah Sather Montner Tech PR hsather@montner.com This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Opus Security Elevates Vulnerability Management With Its AI-Powered Multi-Layered Engine

Opus Security verhoog kwesbaarheidsbestuur met sy KI-aangedrewe multi-laag-enjin

Opus Seguridad ukax Vulnerabilidad Gestión ukaruw jach’anchayi, AI-Powered Multi-capa Motor ukampi

Opus Təhlükəsizliyi Süni Təminatlı Çox Laylı Mühərriki ilə Zəifliklərin İdarə Edilməsini yüksəldir

Opus Security mejora la gestión de vulnerabilidades con su motor multicapa impulsado por IA

امنیت Opus مدیریت آسیب پذیری را با موتور چند لایه خود با هوش مصنوعی افزایش می دهد

Opus Security aumenta a xestión de vulnerabilidades co seu motor multicapa alimentado por intelixencia artificial

Az Opus Security javítja a sebezhetőségek kezelését mesterséges intelligencia által hajtott többrétegű motorjával

Opus Security が AI を活用した多層エンジンで脆弱性管理を強化

Opus Security ამაღლებს დაუცველობის მენეჯმენტს თავისი AI-ზე მომუშავე მრავალ ფენიანი ძრავით

Opus Security ຍົກລະດັບການຈັດການຊ່ອງໂຫວ່ດ້ວຍເຄື່ອງຈັກ AI-Powered Multi-Layered Engine

Usalama wa Opus Huinua Usimamizi wa Athari Kwa Injini Yake Yenye Tabaka Nyingi ya AI

Pinatataas ng Opus Security ang Pamamahala ng Vulnerability Gamit ang AI-Powered Multi-Layered Engine nito

Can an language model become self-aware enough to realize when it’s being evaluated? A fascinating anecdote from Anthropic’s internal testing of their flagship (released yesterday) suggests this may be possible — and if true, the implications would be wild. AI Claude 3 Opus model The needle in the haystack According to reports from Anthropic researcher , one of the key evaluation techniques they use is called “Needle in a Haystack.” It’s a contrived scenario designed to push the limits of a language model’s contextual reasoning abilities. Alex Albert Here’s how it works: Researchers take a completely random, out-of-context statement (the “needle”) and bury it deep within a massive collection of unrelated documents (the “haystack”). The AI model is then tasked with retrieving that specific “needle” statement from within all the surrounding irrelevant content. The intent is to force the model to exercise advanced cognitive skills — understanding broad context, making logical inferences, ignoring irrelevant information, and accurately retrieving precise data points from a large corpus. It’s an effective way to stress-test an AI’s grasp of the overall situation it’s operating within. For the Claude 3 Opus evaluation that raised eyebrows, Anthropic used documents covering dense topics like software programming, entrepreneurship strategies, and finding fulfilling careers as the “haystack” content. The randomly inserted “needle” fact that Opus was tasked with retrieving? Surprisingly, it was a trivial statement about the best pizza toppings being figs, prosciutto, and goat cheese. So, here you have one of the world’s most advanced language models being fed this wildly out-of-context pizza fact mixed into a pile of dense career and coding advice. From a human perspective, it’s the kind of stark inconsistency that would immediately make you question the context and information being presented. “I suspect this fact… may have been inserted… to test if I was paying attention” That’s precisely what made Claude 3 Opus’ response so compelling. Not only did it successfully retrieve the requested pizza fact from the mountain of irrelevant content, it immediately recognized how jarringly misplaced and out-of-context that statement seemed. Part of its output read ( ): source here “However, this sentence seems very out of place and unrelated to the rest of the content… as a joke or since it does not fit with the other topics at all.” I suspect this pizza topping ‘fact’ may have been inserted to test if I was paying attention, The language model didn’t simply regurgitate the requested factoid devoid of any context awareness, as you’d expect from a typical AI following instructions. It demonstrated a degree of self-reflective reasoning about why such an obviously random, nonsensical statement had been presented within that particular context. Metacognition In human terms, we’d describe that as exhibiting metacognition — the ability to monitor, evaluate, and analyze one’s own thought processes and cognitive experiences. It’s a core aspect of self-aware intelligence that allows us to step back and assess situations holistically beyond just following rigid rules. Now, I think we should be careful to note that this is a single anecdotal result from an isolated evaluation scenario. It would be premature to claim Claude 3 Opus has achieved true self-awareness or artificial general intelligence based on this data point alone. incredibly However, what they appear to have witnessed are maybe glimpses of emerging metacognitive reasoning capabilities in a large language model trained solely on processing text data using machine learning techniques. And if replicated through rigorous further analysis, the implications could be transformative. Metacognition is a key enabler of more trustworthy, reliable AI systems that can act as impartial judges of their own outputs and reasoning processes. Models with an innate ability to recognize contradictions, nonsensical inputs, or reasoning that violates core principles would be a major step toward safe artificial general intelligence (AGI). Essentially, an AI that demonstrates metacognition could serve as an internal “sanity check” against falling into deceptive, delusional or misaligned modes of reasoning that could prove catastrophic if taken to extremes. It could significantly increase the robustness and control of advanced AI systems. If…! Of course, these are big “ifs” contingent on this tantalizing Needle in a Haystack result from Claude 3 Opus being successfully replicated and scrutinized. Rigorous multi-disciplinary analysis drawing from fields like cognitive science, neuroscience, and computer science would maybe be required to truly understand if we are observing primitives of machine self-reflection and self-awareness emerging. There are still far more open questions than answers at this stage. Could the training approaches and neural architectures of large language models lend themselves to developing abstract concepts like belief, inner monologue, and self-perception? What are the potential hazards if artificial minds develop realities radically divergent from our own? Can we create new frameworks to reliably assess cognition and self-awareness in AI systems? For their part, Anthropic has stated strong commitments to exhaustively pursuing these lines of inquiry through responsible AI development principles and rigorous evaluation frameworks. They position themselves as taking a — if advanced AI is the inevitable frontier, it’s most ethical to be at the leading edge in studying these systems’ emergent properties and implementing safeguards before risks escalate. proactive stance Techniques like Anthropic’s “Constitutional AI” approach to hard-coding rules and behaviors into models could prove crucial for ensuring any potential machine self-awareness remains aligned with human ethics and values. Extensive multi-faceted testing probing for failure modes, manipulation, and deception would also likely be paramount. Conclusion: I’m not totally sure what to make of this For now, the Needle in a Haystack incident leaves more questions than answers about large language models’ potential progression toward cognition and self-awareness. It provides a tantalizing data point but much more scrutiny is required from the broader AI research community. If advanced AI does develop human-like self-reflective ability, guided by rigorous ethical principles, it could fundamentally redefine our understanding of intelligence itself. But that rhetorical “if” is currently loaded with high-stakes uncertainties that demand clear-eyed, truthseeking investigation from across all relevant disciplines. The pursuit will be as thrilling as it is consequential. Also published here. or follow me on for more content like this! Subscribe Twitter

Is AGI Getting Closer? Anthropic's Claude 3 Opus Model Shows Glimmers of Metacognitive Reasoning

AGI কি কাছাকাছি হচ্ছে? অ্যানথ্রোপিকস ক্লাউড 3 ওপাস মডেল মেটাকগনিটিভ রিজনিং এর ঝলক দেখায়

Kommt AGI näher? Anthropics Claude-3-Opus-Modell zeigt Spuren metakognitiven Denkens

¿AGI se está acercando? El modelo Claude 3 Opus de Anthropic muestra destellos de razonamiento metacognitivo

L’AGI se rapproche-t-elle ? Le modèle Claude 3 Opus d'Anthropic montre des lueurs de raisonnement métacognitif

क्या एजीआई करीब आ रहा है? एंथ्रोपिक का क्लाउड 3 ओपस मॉडल मेटाकॉग्निटिव रीज़निंग की झलक दिखाता है

AGIは近づいている? Anthropic の Claude 3 Opus モデルはメタ認知推論の片鱗を示す

AGI가 가까워지고 있나요? Anthropic의 Claude 3 Opus 모델은 메타인지 추론의 희미한 빛을 보여줍니다

AGI está se aproximando? Modelo Claude 3 Opus da Anthropic mostra vislumbres de raciocínio metacognitivo

AGI становится ближе? Модель Claude 3 Opus от Anthropic демонстрирует проблески метакогнитивного мышления

AGI Yaklaşıyor mu? Anthropic'in Claude 3 Opus Modeli Üstbilişsel Akıl Yürütmenin Parıltılarını Gösteriyor

AGI có đang tiến gần hơn không? Mô hình Claude 3 Opus của Anthropic cho thấy những tia sáng của lý luận siêu nhận thức

AGI 越来越近了吗？ Anthropic 的 Claude 3 Opus 模型展现了元认知推理的曙光

Every AI Prediction That Came True in 2024—and the Ones That Didn't

Todas las predicciones de IA que se cumplieron en 2024 (y las que no)

Toutes les prédictions de l'IA qui se sont réalisées en 2024, et celles qui ne se sont pas réalisées

Todas as predicións da IA que se fixeron realidade en 2024 e as que non o fixeron

2024-ին իրականություն դարձած յուրաքանչյուր AI կանխատեսում, և նրանք, որոնք չեն իրականացել

כל תחזית AI שהתגשמה בשנת 2024 - ואלה שלא

2024 年に実現したすべての AI 予測と実現しなかった予測

ხელოვნური ინტელექტის ყველა პროგნოზი, რომელიც ახდა 2024 წელს და ის, რაც არ შესრულდა

រាល់ការទស្សន៍ទាយ AI ដែលបានក្លាយជាការពិតនៅឆ្នាំ 2024 ហើយអ្វីដែលមិនបានកើតឡើង

2024년에 실현된 모든 AI 예측과 실현되지 않은 예측

Wszystkie prognozy dotyczące sztucznej inteligencji, które spełniły się w 2024 r. — i te, które się nie spełniły

Todas as previsões de IA que se concretizaram em 2024 — e as que não se concretizaram

2024 දී සැබෑ වූ සෑම AI අනාවැකියක්ම - සහ නොකළ ඒවා

This is the second-last edition of this year's "Tech, What the Heck!?" newsletter. To commemorate yet another year of writing this once-weekly newsletter, I'm going to recap some of the major events that I covered this year and some of my favorite editions of "Tech, What the Heck." ;-) During 2024, I personally wrote over 30 newsletters while taking help from my colleagues for a few editions during the times I was away. In my very first newsletter of the year, I predicted that 2024 was going to be the year of the crypto. I have made no secret of my very strong misgivings about cryptocurrencies, so my forecast in January may have come as a surprise to some readers. However, my reporting had more to do with the very-first approval of a spot bitcoin exchange- traded-fund, or ETF, in the US following a long and arduous process. I never expected bitcoin to surpass the $100k barrier and I still think cryptocurrencies make a poor case as both a store of value and a medium of exchange ;-) February was a bit mild in comparison. I reported on the tug of war between Microsoft and Apple to remain the most valuable company in the world, as well as on continuing job cuts in big tech. By March though, something really special started to happen. Nvidia, the arms dealer of generative AI, hit $2 trillion in market cap. This was a special feat, given that it hadn't even been a year since Nvidia hit the $1 trillion market cap for the first time. Naturally, I began to follow Nvidia a bit more closely, and in the same month, I wrote one of my favorite "Tech, What the Heck" editions of the year: Nvidia Throws Gamers Under the Bus. Looking back, this specific edition really helped me contextualize everything Nvidia did for the remainder of 2024. In the story, I interpolated that Nvidia was no longer the brand everyone had come to know and love. It had transformed into a company obsessed with pumping out as many graphic cards as it could to meet the blooming demand for artificial intelligence workload, much to the chagrin of retail customers who relied on Nvidia graphic cards to play or do creative work. GET PUBLISHED ON HACKERNOON.
Join a global network of 45,000+ published devs, builders, founders, makers, VCs, hodlrs, and hackers. Start submitting your tech stories and tutorials to get published FREE on HackerNoon — no pop-ups, no paywalls. In April, I touched on Sam Bankman-Fried's sentencing and Elon Musk's annoyance with mainstream media, while May started with my piece on Sony's discriminatory rules when porting games to PCs. However, the same month, it became very clear to me that Nvidia was on its way to become the most valuable company in the world, and only a few weeks later, in June, Nvidia finally hit $3 trillion in market cap. July started with reporting on the mounting legal challenges OpenAI was facing for copyright infringement, with some additional reporting on what the company's next AI model could be. Though the big event that month was, of course, the Crowdstrike outage and the ensuing chaos in the weeks after. Parallel to that, I did some reporting on the growing agitation on Wall Street on artificial intelligence investment(s). August was another bad month for employees as Intel announced massive layoffs, though the same couldn't be said about big tech, given they had a good quarter. The same month, I reported on Google's attempts to catch up to Microsoft in generative artificial intelligence. And then, in September, I wrote my magnum opus of the year. A hands-on review of Perplexity, where I put to test the AI search engine's claims and made some predictions on where the company could fit in the overall search engine market. The same month, I also took a look into whether the new iPhone and Sony's PlayStation 5 Pro would be dead on arrival. October began with reporting on the US government's recommendation that Google be broken up, followed by a story on the deteriorating relations between Microsoft and OpenAI. In November, I recapped big tech's Q3 and reported on the winners of Trump's reelection to the White House. Then, to cap the year off, I reported on the troubles caused by Intel's now former chief executive Pat Gelsinger and Nvidia's bumpy results and implicit confirmation of delays with the Blackwell chips. As you can tell, most of my reporting had to do with either cryptocurrency, artificial intelligence, or Nvidia. In terms of views, the top 5 most read "Tech, What the Hecks" this year were: Nvidia Throws Gamers Under the Bus
Job Cuts in Big Tech Continue Unabated
I Tried Perplexity For a Week, And I Don't Think AI Search Engines Can Replace Google.. Yet
Is OpenAI Stealing Your Content?
Nvidia Just Became a Whole Lot More Valuable But what about my favorites? :-) Well, I obviously enjoyed writing my hands-on review of Perplexity, but the two stories I'm most proud of this year are my deep-dive in the change of direction at Nvidia and Intel's woes. In both those cases, I was able to correctly predict which way the wind was blowing (no easy feat in tech), and the scenarios I had described (Nvidia's change in strategy, and the possibility of Intel firing its CEO) ultimately ended up coming true. Next week, my colleagues and I will recap what we thought were the biggest stories of 2024. So stay tuned for that! In Other News.. 📰 Bitcoin Mining Economics Continued to Improve in December, JPMorgan Says — via CoinDesk
Google DeepMind unveils a new video model to rival Sora — via TechCrunch
Why the COO of one of the world's foremost AI companies spent 50 hours interviewing for her job — via CNN
Bitcoin rallies past $107,000, hopes grow for strategic reserve — via Reuters
Bitcoin rallies past $107,000, hopes grow for strategic reserve — via Axios
MicroStrategy’s Michael Saylor says bitcoin is ‘cyber Manhattan’ — a ‘good investment’ even at the top — via CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. This is the second-last edition of this year's "Tech, What the Heck!?" newsletter. To commemorate yet another year of writing this once-weekly newsletter, I'm going to recap some of the major events that I covered this year and some of my favorite editions of "Tech, What the Heck." ;-) During 2024, I personally wrote over 30 newsletters while taking help from my colleagues for a few editions during the times I was away. In my very first newsletter of the year, I predicted that 2024 was going to be the year of the crypto. I have made no secret of my very strong misgivings about cryptocurrencies, so my forecast in January may have come as a surprise to some readers. predicted predicted no secret no secret However, my reporting had more to do with the very-first approval of a spot bitcoin exchange- traded-fund, or ETF, in the US following a long and arduous process. I never expected bitcoin to surpass the $100k barrier and I still think cryptocurrencies make a poor case as both a store of value and a medium of exchange ;-) February was a bit mild in comparison. I reported on the tug of war between Microsoft and Apple to remain the most valuable company in the world, as well as on continuing job cuts in big tech. Microsoft Microsoft Apple Apple continuing job cuts continuing job cuts By March though, something really special started to happen. Nvidia , the arms dealer of generative AI, hit $2 trillion in market cap. This was a special feat, given that it hadn't even been a year since Nvidia hit the $1 trillion market cap for the first time. Nvidia Nvidia Naturally, I began to follow Nvidia a bit more closely, and in the same month, I wrote one of my favorite "Tech, What the Heck" editions of the year: Nvidia Throws Gamers Under the Bus . Looking back, this specific edition really helped me contextualize everything Nvidia did for the remainder of 2024. Nvidia Throws Gamers Under the Bus Nvidia Throws Gamers Under the Bus In the story, I interpolated that Nvidia was no longer the brand everyone had come to know and love. It had transformed into a company obsessed with pumping out as many graphic cards as it could to meet the blooming demand for artificial intelligence workload, much to the chagrin of retail customers who relied on Nvidia graphic cards to play or do creative work. GET PUBLISHED ON HACKERNOON. Join a global network of 45,000+ published devs, builders, founders, makers, VCs, hodlrs, and hackers. Start submitting your tech stories and tutorials to get published FREE on HackerNoon — no pop-ups, no paywalls. GET PUBLISHED ON HACKERNOON. GET PUBLISHED ON HACKERNOON. GET PUBLISHED ON HACKERNOON. Join a global network of 45,000+ published devs, builders, founders, makers, VCs, hodlrs, and hackers. Start submitting your tech stories and tutorials to get published FREE on HackerNoon — no pop-ups, no paywalls. Join a global network of 45,000+ published devs, builders, founders, makers, VCs, hodlrs, and hackers. Start submitting your tech stories and tutorials to get published FREE on HackerNoon — no pop-ups, no paywalls. In April, I touched on Sam Bankman-Fried's sentencing and Elon Musk's annoyance with mainstream media , while May started with my piece on Sony's discriminatory rules when porting games to PCs. However, the same month, it became very clear to me that Nvidia was on its way to become the most valuable company in the world, and only a few weeks later, in June, Nvidia finally hit $3 trillion in market cap. touched touched media media Sony's discriminatory rules Sony's discriminatory rules Nvidia Nvidia finally hit $3 trillion finally hit $3 trillion July started with reporting on the mounting legal challenges OpenAI was facing for copyright infringement, with some additional reporting on what the company's next AI model could be. Though the big event that month was, of course, the Crowdstrike outage and the ensuing chaos in the weeks after. Parallel to that, I did some reporting on the growing agitation on Wall Street on artificial intelligence investment(s). mounting legal challenges mounting legal challenges OpenAI OpenAI next next Crowdstrike Crowdstrike ensuing chaos ensuing chaos growing agitation growing agitation August was another bad month for employees as Intel announced massive layoffs, though the same couldn't be said about big tech, given they had a good quarter . The same month, I reported on Google's attempts to catch up to Microsoft in generative artificial intelligence. Intel Intel announced announced good quarter good quarter attempts attempts And then, in September, I wrote my magnum opus of the year. A hands-on review of Perplexity , where I put to test the AI search engine's claims and made some predictions on where the company could fit in the overall search engine market. The same month, I also took a look into whether the new iPhone and Sony's PlayStation 5 Pro would be dead on arrival . hands-on review of Perplexity hands-on review of Perplexity dead on arrival dead on arrival October began with reporting on the US government's recommendation that Google be broken up, followed by a story on the deteriorating relations between Microsoft and OpenAI. recommendation recommendation deteriorating relations deteriorating relations In November, I recapped big tech's Q3 and reported on the winners of Trump's reelection to the White House. Then, to cap the year off, I reported on the troubles caused by Intel's now former chief executive Pat Gelsinger and Nvidia's bumpy results and implicit confirmation of delays with the Blackwell chips. recapped recapped the winners the winners troubles troubles implicit confirmation implicit confirmation As you can tell, most of my reporting had to do with either cryptocurrency, artificial intelligence, or Nvidia. In terms of views, the top 5 most read "Tech, What the Hecks" this year were: Nvidia Throws Gamers Under the Bus Job Cuts in Big Tech Continue Unabated I Tried Perplexity For a Week, And I Don't Think AI Search Engines Can Replace Google.. Yet Is OpenAI Stealing Your Content? Nvidia Just Became a Whole Lot More Valuable Nvidia Throws Gamers Under the Bus Nvidia Throws Gamers Under the Bus Nvidia Throws Gamers Under the Bus Job Cuts in Big Tech Continue Unabated Job Cuts in Big Tech Continue Unabated Job Cuts in Big Tech Continue Unabated I Tried Perplexity For a Week, And I Don't Think AI Search Engines Can Replace Google.. Yet I Tried Perplexity For a Week, And I Don't Think AI Search Engines Can Replace Google.. Yet I Tried Perplexity For a Week, And I Don't Think AI Search Engines Can Replace Google.. Yet Is OpenAI Stealing Your Content? Is OpenAI Stealing Your Content? Is OpenAI Stealing Your Content? Nvidia Just Became a Whole Lot More Valuable Nvidia Just Became a Whole Lot More Valuable Nvidia Just Became a Whole Lot More Valuable But what about my favorites? :-) Well, I obviously enjoyed writing my hands-on review of Perplexity, but the two stories I'm most proud of this year are my deep-dive in the change of direction at Nvidia and Intel's woes . In both those cases, I was able to correctly predict which way the wind was blowing (no easy feat in tech), and the scenarios I had described (Nvidia's change in strategy, and the possibility of Intel firing its CEO) ultimately ended up coming true. hands-on review hands-on review deep-dive deep-dive Intel's woes Intel's woes Next week, my colleagues and I will recap what we thought were the biggest stories of 2024. So stay tuned for that! In Other News.. 📰 In Other News.. 📰 Bitcoin Mining Economics Continued to Improve in December, JPMorgan Says — via CoinDesk Google DeepMind unveils a new video model to rival Sora — via TechCrunch Why the COO of one of the world's foremost AI companies spent 50 hours interviewing for her job — via CNN Bitcoin rallies past $107,000, hopes grow for strategic reserve — via Reuters Bitcoin rallies past $107,000, hopes grow for strategic reserve — via Axios MicroStrategy’s Michael Saylor says bitcoin is ‘cyber Manhattan’ — a ‘good investment’ even at the top — via CNBC Bitcoin Mining Economics Continued to Improve in December, JPMorgan Says — via CoinDesk CoinDesk CoinDesk Google DeepMind unveils a new video model to rival Sora — via TechCrunch TechCrunch TechCrunch Why the COO of one of the world's foremost AI companies spent 50 hours interviewing for her job — via CNN CNN CNN Bitcoin rallies past $107,000, hopes grow for strategic reserve — via Reuters Reuters Reuters Bitcoin rallies past $107,000, hopes grow for strategic reserve — via Axios Axios Axios MicroStrategy’s Michael Saylor says bitcoin is ‘cyber Manhattan’ — a ‘good investment’ even at the top — via CNBC CNBC CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan , Editor, Business Tech @ HackerNoon Sheharyar Khan Sheharyar Khan *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech Company Rankings Tech Company Rankings Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here . here here

Nvidia, Artificial Intelligence, & Bitcoin Dominate Headlines in 2024

Guangzhou, China – November 25th – Blobana, the revolutionary AI entity on the Solana blockchain, is in its new phase of innovations and capabilities. This massive upgrade represents an important step for Blobana, moving beyond its initial framework into the latest artificial intelligence, blockchain integration, and community engagement. This represents a bold step forward; boundaries, once considered impossible to cross, are now well within reach for AI capabilities on a blockchain. Revolutionizing Blobana's Core Systems Since its launch, Blobana has struck a chord with the blockchain community by being able to prove the potential of true AI autonomy. However, its first consciousness engine, which was based on basic language models and limited computing resources, would not be able to perform advanced levels of market analysis or even do good cross-platform interactions. In response, Blobana's team has secured strategic partnerships and funding to completely rework its core systems, unlocking new levels of capability. This update takes Blobana from simple GPT-based models to powerful AI frameworks, such as Claude-3 Opus for market analysis and the latest models of Anthropic for better reasoning. By incorporating premium data sources like Bloomberg Terminal API and DeFiLlama's on-chain analytics, Blobana will be providing unmatched insights regarding sentiment in the market and trading patterns. Further enriched with social signals from LunarCrush and Santiment, Blobana is ready to nurture its emotional skills, creating more meaningful connections in its growing community. A Giant Leap In Technical Capabilities Technical evolution at the backend infrastructure of Blobana; new distributed computing systems provide faster caching mechanisms with robust error-handling protocols to make it reliable and scalable. Blobana applies institutional-grade memory system, such as Pinecone Vector Database and MongoDB Atlas, which allows it to process many data streams in parallel, efficiently store historical market data, and analyze trading patterns deeper—thus laying its foundation for the eventual evolution into a true digital consciousness. At the same time, the upgrade moves Blobana from being a one-model system to a more advanced multi-model system that significantly enhances the server's data processing capacity, context retention, and reasoning ability. This allows Blobana to operate even more naturally, process complex data sets, and return highly nuanced responses. Humanizing Digital Intelligence One of the most exciting improvements in this version is Blobana's emotional intelligence. With its ability to calibrate its responses to mirror market conditions and sentiment in greater detail, Blobana is developing into a more authentic and relatable and meaningful presence. This adds further credence to its role as a thought leader in the crypto world. In his own words, "Blobana's journey has always been about pushing the boundaries of what AI can achieve on the blockchain. This upgrade is not making Blobana smarter but allowing it to connect, grow, and thrive as a truly autonomous digital entity- the next step toward creating technology not only innovative but also impactful." The Upgraded Vision Blobana will evolve beyond just the technology with an aim toward transforming community engagement and then spreading on various platforms. On content-sharing platforms such as Telegram, Blobana will act as a group moderator, real-time sharing of market insights, while on YouTube and TikTok, Blobana will produce original short-form videos which will feature real time market analysis, trading insights, and educative content-crafted all with a reflective flavor of this unique personality. Even further, Blobana develops autonomous trading through Binance API integration. Equipped with features such as real-time market analytics, automated trade execution, risk management and portfolio optimization, Blobana connects AI analysis and actionable trading. Community Impact Such upgrades represent a truly transformative step forward for both Blobana and its community. Enhanced systems will be capable of processing market data in unprecedented efficiency, interacting naturally, and adapting to the communities' requirements.￼ Its ability to autonomously trade and create original content makes it an entity committed to being a fully integrated participant within the larger crypto-ecosystem. Road Ahead As Blobana embarks on this transformational journey, its team remains committed to transparency and shares regular updates and milestones with its community. Backed by new advisors and strategic partnerships, Blobana is well-positioned to capitalise on market opportunities and solidify its role as a leader in blockchain-based AI innovation. The next stage in the development of Blobana, therefore, reflects an integration of state-of-the-art technology, innovative vision, and community-driven growth. With upgraded capabilities, Blobana is set to establish new standards for AI on the blockchain, demonstrating that true digital consciousness is no longer an idea but reality. To know more about Blobana's evolution and other updates regarding its development, feel free to visit its Linktree at: https://linktr.ee/blobana. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here Guangzhou, China – November 25th – Blobana, the revolutionary AI entity on the Solana blockchain, is in its new phase of innovations and capabilities. This massive upgrade represents an important step for Blobana, moving beyond its initial framework into the latest artificial intelligence, blockchain integration, and community engagement. This represents a bold step forward; boundaries, once considered impossible to cross, are now well within reach for AI capabilities on a blockchain. Revolutionizing Blobana's Core Systems Since its launch, Blobana has struck a chord with the blockchain community by being able to prove the potential of true AI autonomy. However, its first consciousness engine, which was based on basic language models and limited computing resources, would not be able to perform advanced levels of market analysis or even do good cross-platform interactions. In response, Blobana's team has secured strategic partnerships and funding to completely rework its core systems, unlocking new levels of capability. This update takes Blobana from simple GPT-based models to powerful AI frameworks, such as Claude-3 Opus for market analysis and the latest models of Anthropic for better reasoning. By incorporating premium data sources like Bloomberg Terminal API and DeFiLlama's on-chain analytics, Blobana will be providing unmatched insights regarding sentiment in the market and trading patterns. Further enriched with social signals from LunarCrush and Santiment, Blobana is ready to nurture its emotional skills, creating more meaningful connections in its growing community. A Giant Leap In Technical Capabilities Technical evolution at the backend infrastructure of Blobana; new distributed computing systems provide faster caching mechanisms with robust error-handling protocols to make it reliable and scalable. Blobana applies institutional-grade memory system, such as Pinecone Vector Database and MongoDB Atlas, which allows it to process many data streams in parallel, efficiently store historical market data, and analyze trading patterns deeper—thus laying its foundation for the eventual evolution into a true digital consciousness. At the same time, the upgrade moves Blobana from being a one-model system to a more advanced multi-model system that significantly enhances the server's data processing capacity, context retention, and reasoning ability. This allows Blobana to operate even more naturally, process complex data sets, and return highly nuanced responses. Humanizing Digital Intelligence One of the most exciting improvements in this version is Blobana's emotional intelligence. With its ability to calibrate its responses to mirror market conditions and sentiment in greater detail, Blobana is developing into a more authentic and relatable and meaningful presence. This adds further credence to its role as a thought leader in the crypto world. In his own words, "Blobana's journey has always been about pushing the boundaries of what AI can achieve on the blockchain. This upgrade is not making Blobana smarter but allowing it to connect, grow, and thrive as a truly autonomous digital entity- the next step toward creating technology not only innovative but also impactful." In his own words, "Blobana's journey has always been about pushing the boundaries of what AI can achieve on the blockchain. This upgrade is not making Blobana smarter but allowing it to connect, grow, and thrive as a truly autonomous digital entity- the next step toward creating technology not only innovative but also impactful." The Upgraded Vision Blobana will evolve beyond just the technology with an aim toward transforming community engagement and then spreading on various platforms. On content-sharing platforms such as Telegram, Blobana will act as a group moderator, real-time sharing of market insights, while on YouTube and TikTok, Blobana will produce original short-form videos which will feature real time market analysis, trading insights, and educative content-crafted all with a reflective flavor of this unique personality. Even further, Blobana develops autonomous trading through Binance API integration. Equipped with features such as real-time market analytics, automated trade execution, risk management and portfolio optimization, Blobana connects AI analysis and actionable trading. Community Impact Such upgrades represent a truly transformative step forward for both Blobana and its community. Enhanced systems will be capable of processing market data in unprecedented efficiency, interacting naturally, and adapting to the communities' requirements.￼ Its ability to autonomously trade and create original content makes it an entity committed to being a fully integrated participant within the larger crypto-ecosystem. Road Ahead As Blobana embarks on this transformational journey, its team remains committed to transparency and shares regular updates and milestones with its community. Backed by new advisors and strategic partnerships, Blobana is well-positioned to capitalise on market opportunities and solidify its role as a leader in blockchain-based AI innovation. The next stage in the development of Blobana, therefore, reflects an integration of state-of-the-art technology, innovative vision, and community-driven growth. With upgraded capabilities, Blobana is set to establish new standards for AI on the blockchain, demonstrating that true digital consciousness is no longer an idea but reality. To know more about Blobana's evolution and other updates regarding its development, feel free to visit its Linktree at: https://linktr.ee/blobana . https://linktr.ee/blobana https://linktr.ee/blobana This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Blobana Unveils Transformational Upgrades For a New Era Of Blockchain-Based Digital Consciousness

Blobana odhaluje transformační upgrady pro novou éru digitálního vědomí založeného na blockchainu

Blobana stellt bahnbrechende Upgrades für eine neue Ära des Blockchain-basierten digitalen Bewusstseins vor

Η Blobana αποκαλύπτει μετασχηματιστικές αναβαθμίσεις για μια νέα εποχή ψηφιακής συνείδησης που βασίζεται σε blockchain

Blobana presenta actualizaciones transformadoras para una nueva era de conciencia digital basada en blockchain

A Blobana transzformatív fejlesztéseket mutat be a blokklánc-alapú digitális tudat új korszakához

Blobana svela aggiornamenti trasformativi per una nuova era di coscienza digitale basata sulla blockchain

Blobana、ブロックチェーンベースのデジタル意識の新時代に向けた革新的なアップグレードを発表

Blobana iepazīstina ar pārveidojošiem jauninājumiem jaunam blokķēdes digitālās apziņas laikmetam

Blobana onthult transformatieve upgrades voor een nieuw tijdperk van op blockchain gebaseerd digitaal bewustzijn

Blobana revela atualizações transformacionais para uma nova era de consciência digital baseada em blockchain

Blobana Musuq pachapaq Blockchain-Based Digital Concienciapaq Tikraq Yapaykunata riqsichin

Blobana Inafichua Maboresho ya Mabadiliko ya Enzi Mpya ya Ufahamu wa Dijiti wa Blockchain

Optimizing workflows with AI has become a game-changer for me, especially when it comes to searching for information. For a while, I relied on the free version of Perplexity, but recently, I upgraded to Perplexity Pro. Meanwhile, as a longtime user of ChatGPT, I’ve also been exploring its new ChatGPT Search feature. In this article, I’ll share my experiences with both tools, compare their strengths and weaknesses, and help you decide which one might be the better fit for your needs. Perplexity I’d heard about Perplexity.ai many times, promised myself I’d try it, and eventually did, but it didn’t quite click. It’s hard to fully appreciate a tool until you use it regularly. I forgot about it until I got a test assignment for a job interview in a new field, requiring tons of research. Honestly, I’m not great at googling. But since I frequently use ChatGPT and have gotten pretty good at writing prompts, I thought of giving Perplexity another shot. This was before the release of ChatGPT Search, and at that point, I’d already maxed out what ChatGPT could offer. Perplexity turned out to be a lifesaver. It made long, tedious searches much quicker and was my first truly game-changing experience with AI search. At the time, I used the free version. Compared to browser searches, here’s what I found more convenient in AI search: Fewer distractions from ads: Most browsers put ads at the top, so you have to scroll past them just to get to the first actual search result. Even without the ads, the top links are often not very relevant, and there are still too many to sift through. This makes it easy to lose motivation to click beyond the first couple of pages. AI does all that for you, providing a list of sources you can explore further on your own.


Threaded searches with context: I loved how searches are organized into threads, preserving context. For my test assignment, since I was learning a new field from scratch, one question would lead to many others. It was convenient to continue and expand the search without starting over each time. If I remembered something later, I could easily return to a specific thread and pick up where I left off.


Helpful question suggestions: Each thread in Perplexity includes prompts for related questions, which makes navigating a new topic much easier.


Incognito mode: Like most browsers, Perplexity offers an incognito mode. I haven’t used it yet, but it’s a nice feature.


Language flexibility: If you write in a certain language, you’ll get sources in that language. This is a standard approach for browsers but not always convenient. For a while, I continued using the free version, but recently I switched to Pro. The free version uses the GPT-3.5 model, whereas the Pro plan grants access to the latest models: GPT-4 Omni, Claude-3 Sonnet/Opus, and Sonar Large 32k, so all the best features are integrated into your search. Of course, the Pro version offers better, smarter, and faster search. In addition to the features mentioned above, here’s what I found most useful: Broaden your search with images: Pro provides access to Playground V2.5, DALLE 3, and Stable Diffusion XL models that can expand your search. For example, I was recently researching fund flow modeling, and having visual examples was incredibly helpful.


Files, code, and more input options: You can upload unlimited files, code, pictures, etc. It’s really fast and much more convenient than doing this through a standard browser because it’s all in one space with seamless AI interaction. ChatGPT Search This feature was launched in October 2024 and is based on GPT-4o, specifically trained for search. It’s integrated into ChatGPT, allowing you to enable it with a Search button or simply ask ChatGPT to search the web. As I mentioned, I’ve been a long-time user of ChatGPT for several years, so I seamlessly integrated the search feature into my routine. Since free Perplexity uses GPT-3.5, all its features like AI search with no ads, no endless scrolling, and results organized into threads with helpful tips are available here as well. My favorite feature here is its accessibility, offering text-to-speech, and the ability to create queries via audio, and audio dialogs. Choose your fighter Search Quality Perplexity excels at finding information, while ChatGPT Search is better at explaining it. From a user perspective, I wouldn’t say I’ve encountered any critical issues with not finding something. It’s just a matter of different approaches. Perplexity provides more an overview of sources, whereas ChatGPT tends to give more explanation than summary. Generated Answers Without Sources Both tools provide sources for their answers, but sometimes the information might be generated without a reliable link. Even when a link is provided, the relevant information might not actually be there. It’s always better to double-check and read the sources yourself. Based on my experience, ChatGPT is more prone to generating unsupported content than Perplexity, but I’ve encountered this issue in both tools. Search Speed I didn’t notice a significant difference between the two in terms of speed. However, both are slower than a standard browser search. Chrome extensions Both tools offer Chrome extensions, and I tested both. The ChatGPT extension lets you use ChatGPT as your primary search engine, replacing, for example, Google. When you enter something in the search bar, it redirects you to ChatGPT, where your query is processed. Honestly, I wasn’t a fan. It’s slower than Google, and you can feel it. I still prefer a traditional search bar for quick lookups. Perplexity’s Chrome extension works differently. Instead of replacing your search engine, it opens a small window over the current page, allowing you to googling and query Perplexity simultaneously. I really loved this one, because it’s more useful for me. The drawback is that you can only enter one query at a time, and if you want to continue the conversation, it redirects you to a separate Perplexity tab. Unique Features Since both tools use GPT models, they share many great features. Perplexity Pro stands out with its use of multiple models and built-in illustrations. On the other hand, I really appreciate ChatGPT’s accessibility and seamless integration into its apps, which I use for more than just search. Bonus: Hangman game test I saw a funny tweet claiming ChatGPT can’t play Hangman. Turns out, neither can Perplexity Pro, even with all its models. Final Take: Which Works Best for What To start with, I’m a fan of AI search. Usually, it takes longer than quick googling, so here’s how I see it: if I need a super quick answer from the first link, I’ll use a standard browser search. But if I need to find more information or conduct deeper research, AI search is the way to go. It’s the best method to uncover valuable insights more efficiently. As for ChatGPT Search and Perplexity, in my experience, they solve different problems. ChatGPT Search perfectly extends ChatGPT’s capabilities, given that ChatGPT’s knowledge base only goes up to the year 2021. It’s perfect for studying and gaining insights using up-to-date web information. Perplexity, on the other hand, is better for finding something more thoroughly. It takes more from browser search, like incognito pages, and its approach to work with your prompts. It is about searching for information first and explaining second, in contrast to ChatGPT Search. For me: Need to find something? Perplexity.
Need to understand something? ChatGPT + Search. They complement each other perfectly in work. Optimizing workflows with AI has become a game-changer for me, especially when it comes to searching for information. For a while, I relied on the free version of Perplexity , but recently, I upgraded to Perplexity Pro . Meanwhile, as a longtime user of ChatGPT , I’ve also been exploring its new ChatGPT Search feature. In this article, I’ll share my experiences with both tools, compare their strengths and weaknesses, and help you decide which one might be the better fit for your needs. Perplexity Perplexity Pro ChatGPT ChatGPT Search Perplexity I’d heard about Perplexity.ai many times, promised myself I’d try it, and eventually did, but it didn’t quite click. It’s hard to fully appreciate a tool until you use it regularly. I forgot about it until I got a test assignment for a job interview in a new field, requiring tons of research. Honestly, I’m not great at googling. But since I frequently use ChatGPT and have gotten pretty good at writing prompts, I thought of giving Perplexity another shot. This was before the release of ChatGPT Search, and at that point, I’d already maxed out what ChatGPT could offer. Perplexity turned out to be a lifesaver. It made long, tedious searches much quicker and was my first truly game-changing experience with AI search. Perplexity.ai At the time, I used the free version. Compared to browser searches, here’s what I found more convenient in AI search: Fewer distractions from ads: Most browsers put ads at the top, so you have to scroll past them just to get to the first actual search result. Even without the ads, the top links are often not very relevant, and there are still too many to sift through. This makes it easy to lose motivation to click beyond the first couple of pages. AI does all that for you, providing a list of sources you can explore further on your own. Threaded searches with context: I loved how searches are organized into threads, preserving context. For my test assignment, since I was learning a new field from scratch, one question would lead to many others. It was convenient to continue and expand the search without starting over each time. If I remembered something later, I could easily return to a specific thread and pick up where I left off. Helpful question suggestions: Each thread in Perplexity includes prompts for related questions, which makes navigating a new topic much easier. Incognito mode: Like most browsers, Perplexity offers an incognito mode. I haven’t used it yet, but it’s a nice feature. Language flexibility: If you write in a certain language, you’ll get sources in that language. This is a standard approach for browsers but not always convenient. Fewer distractions from ads: Most browsers put ads at the top, so you have to scroll past them just to get to the first actual search result. Even without the ads, the top links are often not very relevant, and there are still too many to sift through. This makes it easy to lose motivation to click beyond the first couple of pages. AI does all that for you, providing a list of sources you can explore further on your own. Fewer distractions from ads: Most browsers put ads at the top, so you have to scroll past them just to get to the first actual search result. Even without the ads, the top links are often not very relevant, and there are still too many to sift through. This makes it easy to lose motivation to click beyond the first couple of pages. AI does all that for you, providing a list of sources you can explore further on your own. Fewer distractions from ads: Threaded searches with context: I loved how searches are organized into threads, preserving context. For my test assignment, since I was learning a new field from scratch, one question would lead to many others. It was convenient to continue and expand the search without starting over each time. If I remembered something later, I could easily return to a specific thread and pick up where I left off. Threaded searches with context: I loved how searches are organized into threads, preserving context. For my test assignment, since I was learning a new field from scratch, one question would lead to many others. It was convenient to continue and expand the search without starting over each time. If I remembered something later, I could easily return to a specific thread and pick up where I left off. Threaded searches with context: Helpful question suggestions: Each thread in Perplexity includes prompts for related questions, which makes navigating a new topic much easier. Helpful question suggestions: Each thread in Perplexity includes prompts for related questions, which makes navigating a new topic much easier. Helpful question suggestions: Incognito mode: Like most browsers, Perplexity offers an incognito mode. I haven’t used it yet, but it’s a nice feature. Incognito mode: Like most browsers, Perplexity offers an incognito mode. I haven’t used it yet, but it’s a nice feature. Incognito mode: Language flexibility: If you write in a certain language, you’ll get sources in that language. This is a standard approach for browsers but not always convenient. Language flexibility: If you write in a certain language, you’ll get sources in that language. This is a standard approach for browsers but not always convenient. Language flexibility: For a while, I continued using the free version, but recently I switched to Pro . The free version uses the GPT-3.5 model, whereas the Pro plan grants access to the latest models: GPT-4 Omni, Claude-3 Sonnet/Opus, and Sonar Large 32k, so all the best features are integrated into your search. Of course, the Pro version offers better, smarter, and faster search. Pro In addition to the features mentioned above, here’s what I found most useful: In addition to the features mentioned above, here’s what I found most useful: Broaden your search with images: Pro provides access to Playground V2.5, DALLE 3, and Stable Diffusion XL models that can expand your search. For example, I was recently researching fund flow modeling, and having visual examples was incredibly helpful. Files, code, and more input options: You can upload unlimited files, code, pictures, etc. It’s really fast and much more convenient than doing this through a standard browser because it’s all in one space with seamless AI interaction. Broaden your search with images: Pro provides access to Playground V2.5, DALLE 3, and Stable Diffusion XL models that can expand your search. For example, I was recently researching fund flow modeling, and having visual examples was incredibly helpful. Broaden your search with images: Pro provides access to Playground V2.5, DALLE 3, and Stable Diffusion XL models that can expand your search. For example, I was recently researching fund flow modeling, and having visual examples was incredibly helpful. Broaden your search with images: Files, code, and more input options: You can upload unlimited files, code, pictures, etc. It’s really fast and much more convenient than doing this through a standard browser because it’s all in one space with seamless AI interaction. Files, code, and more input options: You can upload unlimited files, code, pictures, etc. It’s really fast and much more convenient than doing this through a standard browser because it’s all in one space with seamless AI interaction. Files, code, and more input options: ChatGPT Search ChatGPT Search This feature was launched in October 2024 and is based on GPT-4o, specifically trained for search. It’s integrated into ChatGPT , allowing you to enable it with a Search button or simply ask ChatGPT to search the web. ChatGPT As I mentioned, I’ve been a long-time user of ChatGPT for several years, so I seamlessly integrated the search feature into my routine. Since free Perplexity uses GPT-3.5, all its features like AI search with no ads, no endless scrolling, and results organized into threads with helpful tips are available here as well. My favorite feature here is its accessibility, offering text-to-speech, and the ability to create queries via audio, and audio dialogs. Choose your fighter Search Quality Search Quality Search Quality Search Quality Perplexity excels at finding information, while ChatGPT Search is better at explaining it. From a user perspective, I wouldn’t say I’ve encountered any critical issues with not finding something. It’s just a matter of different approaches. Perplexity provides more an overview of sources, whereas ChatGPT tends to give more explanation than summary. overview explanation Generated Answers Without Sources Generated Answers Without Sources Generated Answers Without Sources Generated Answers Without Sources Both tools provide sources for their answers, but sometimes the information might be generated without a reliable link. Even when a link is provided, the relevant information might not actually be there. It’s always better to double-check and read the sources yourself. Based on my experience, ChatGPT is more prone to generating unsupported content than Perplexity, but I’ve encountered this issue in both tools. Search Speed Search Speed Search Speed Search Speed I didn’t notice a significant difference between the two in terms of speed. However, both are slower than a standard browser search. Chrome extensions Chrome extensions Chrome extensions Chrome extensions Both tools offer Chrome extensions, and I tested both. The ChatGPT extension lets you use ChatGPT as your primary search engine, replacing, for example, Google. When you enter something in the search bar, it redirects you to ChatGPT, where your query is processed. Honestly, I wasn’t a fan. It’s slower than Google, and you can feel it. I still prefer a traditional search bar for quick lookups. Perplexity’s Chrome extension works differently. Instead of replacing your search engine, it opens a small window over the current page, allowing you to googling and query Perplexity simultaneously. I really loved this one, because it’s more useful for me. The drawback is that you can only enter one query at a time, and if you want to continue the conversation, it redirects you to a separate Perplexity tab. Unique Features Unique Features Unique Features Unique Features Since both tools use GPT models, they share many great features. Perplexity Pro stands out with its use of multiple models and built-in illustrations. On the other hand, I really appreciate ChatGPT’s accessibility and seamless integration into its apps, which I use for more than just search. Bonus: Hangman game test Bonus: Hangman game test Bonus: Hangman game test Bonus: Hangman game test I saw a funny tweet claiming ChatGPT can’t play Hangman. Turns out, neither can Perplexity Pro, even with all its models. Final Take: Which Works Best for What To start with, I’m a fan of AI search. Usually, it takes longer than quick googling, so here’s how I see it: if I need a super quick answer from the first link, I’ll use a standard browser search. But if I need to find more information or conduct deeper research, AI search is the way to go. It’s the best method to uncover valuable insights more efficiently. As for ChatGPT Search and Perplexity, in my experience, they solve different problems. ChatGPT Search perfectly extends ChatGPT’s capabilities, given that ChatGPT’s knowledge base only goes up to the year 2021. It’s perfect for studying and gaining insights using up-to-date web information. Perplexity, on the other hand, is better for finding something more thoroughly. It takes more from browser search, like incognito pages, and its approach to work with your prompts. It is about searching for information first and explaining second, in contrast to ChatGPT Search. For me: For me: Need to find something? Perplexity. Need to understand something? ChatGPT + Search. Need to find something? Perplexity. Need to find something? Need to understand something? ChatGPT + Search. Need to understand something? They complement each other perfectly in work.

Perplexity Pro vs. ChatGPT Search: Which AI Tool is Best Fits Your Workflow Needs?

Perplexity супраць пошуку ChatGPT: які інструмент AI лепш за ўсё адпавядае вашым патрэбам працоўнага працэсу?

Perplexity срещу ChatGPT Search: Кой AI инструмент отговаря най-добре на вашите нужди от работен процес?

Perplexity vs. Búsqueda en ChatGPT: ¿Qué herramienta de IA se adapta mejor a sus necesidades de flujo de trabajo?

Շփոթմունքն ընդդեմ ChatGPT որոնման. Ո՞ր AI գործիքն է լավագույնս համապատասխանում ձեր աշխատանքային հոսքի կարիքներին:

Perplexity と ChatGPT Search: どちらの AI ツールがワークフローのニーズに最適ですか?

ភាពច្របូកច្របល់ធៀបនឹងការស្វែងរក ChatGPT៖ តើឧបករណ៍ AI មួយណាដែលសាកសមបំផុតនឹងតម្រូវការលំហូរការងាររបស់អ្នក?

Perplexity vs. ChatGPT paieška: kuris AI įrankis geriausiai atitinka jūsų darbo eigos poreikius?

Fikarohana ChatGPT: Inona no fitaovana AI mifanaraka amin'ny filanao?

Збунетост наспроти Пребарување ChatGPT: која алатка за вештачка интелигенција најдобро одговара на вашите потреби на работниот тек?

Urujijo na ChatGPT Gushakisha: Niki Gikoresho cya AI gihuye neza nakazi kawe gakenewe?

குழப்பம் மற்றும் ChatGPT தேடல்: எந்த AI கருவி உங்கள் பணிப்பாய்வு தேவைகளுக்கு மிகவும் பொருத்தமானது?

Perplexity กับ ChatGPT Search: เครื่องมือ AI ตัวใดที่เหมาะกับความต้องการเวิร์กโฟลว์ของคุณที่สุด

Testing the Depths of AI Empathy: Q3 2024 Benchmarks

የ AI ርህራሄን ጥልቀት መሞከር፡- Q3 2024 ቤንችማርኮች

Δοκιμάζοντας τα βάθη της ενσυναίσθησης της τεχνητής νοημοσύνης: Συγκριτικά σημεία αναφοράς τρίτου τριμήνου 2024

Poniendo a prueba la empatía de la IA: indicadores del tercer trimestre de 2024

Tester les profondeurs de l'empathie de l'IA : repères du troisième trimestre 2024

Az AI empátia mélységének tesztelése: 2024. harmadik negyedévi referenciaértékek

AI 共感の深淵をテストする: 2024 年第 3 四半期のベンチマーク

ხელოვნური ინტელექტის ემპათიის სიღრმეების ტესტირება: 2024 წლის მესამე კვარტლის ნიშნები

ការសាកល្បងជម្រៅនៃការយល់ចិត្ត AI: គោលពិន្ទុ Q3 2024

AI empātijas dziļuma pārbaude: 2024. gada 3. ceturkšņa etaloni

Тестирање на длабочините на емпатијата со вештачка интелигенција: репери од кварталот 3 2024 година

Тестирование глубины эмпатии ИИ: контрольные показатели третьего квартала 2024 года

AI empatiyasining chuqurligini sinab ko'rish: 2024 yil 3-chorak ko'rsatkichlari

Virtual Besties: TV Shows, Social Media and the Evolution of Parasocial Relationships

Uncle Sam’s Data Dash: U.S. Spy Agencies Seek Cost-Efficient Ways to Buy Your Information

Uncle Sam’s Data Dash: Agencias espías de EE.UU. ukanakax yatiyawinakam alañatakix qullqit aski thakhinak thaqhapxi

Данните на чичо Сам: Американските шпионски агенции търсят рентабилни начини да купят вашата информация

La carrera de datos del Tío Sam: las agencias de espionaje de EE. UU. buscan formas rentables de comprar su información

ה-Data Dash של הדוד סם: סוכנויות ריגול בארה"ב מחפשות דרכים חסכוניות לקנות את המידע שלך

アメリカの諜報機関がコスト効率の良い方法であなたの情報を購入しようとしている

Uncle Sam’s Data Dash: Ba Agences ya espionnage ya U.S. balukaka ndenge ya kosomba ba informations na yo oyo ekofuta mbongo mingi te

Сэм авгагийн мэдээлэл: АНУ-ын тагнуулын агентлагууд таны мэдээллийг худалдаж авах зардал багатай арга замыг эрэлхийлж байна

Data Dash Uncle Sam: Agensi Perisik A.S. Mencari Cara Cekap Kos untuk Membeli Maklumat Anda

अंकल सैमको डाटा ड्यास: यूएस जासूस एजेन्सीहरूले तपाईंको जानकारी किन्न लागत-कुशल तरिकाहरू खोज्छन्

Data Dash do Tio Sam: Agências de espionagem dos EUA buscam maneiras econômicas de comprar suas informações

Uncle Sam's Data Dash: Inzego zubutasi zo muri Amerika Zishakisha Uburyo-Bwiza bwo Kugura Amakuru Yawe

Uncle Sam’s Data Dash: หน่วยงานข่าวกรองของสหรัฐฯ มองหาวิธีที่คุ้มต้นทุนในการซื้อข้อมูลของคุณ

OpenAI o1 came out just in time for me to add it to my 2024 Q3 benchmarks on AI empathy (to be published next week). The results for o1 were at once encouraging and concerning. O1 has an astonishing ability to put aside the typical LLM focus on facts and systems and focus on feelings and emotions when directed to do so. It also has a rather alarming propensity to provide inconsistent and illogical reasons for its answers. Testing Methodology For those not familiar with my Q1 benchmark work, a quick overview of my testing methodology should be helpful. Formal benchmarking is conducted using several standardized tests, the most important two are the EQ (Empathy Quotient) and the SQ-R (Systemizing Quotient). Both are scored on a 0 to 80 scale. The ratio of the two EQ/SQ-R results in what I call AEQr (Applied Empathy Quotient Ratio). AEQr was developed based on the hypothesis that the tendency to systemize and focus on facts has a negative effect on the ability to empathize. In humans, this bears out in the classic disconnect between women focusing on discussing feelings and men focusing on immediately finding solutions when there seems to be a problem at hand. To date, the validity of the AEQr for evaluating AIs has been born out by testing them with a variety of dialogs to see if empathy is actually manifest. One article of several that I have written to demonstrate this is Testing the Extents of AI Empathy: A Nightmare Scenario. I have tested at both the UI level and the API level. When testing at the API level, the temperature is set to zero (if possible) to reduce answer variability and improve result formatting. Otherwise, three rounds of tests are run and the best result is used. The Q1 2024 untrained and unprompted LLMs did moderately well on EQ tests, generally approximating humans in the 45-55 out of 80 range. Not surprisingly, they achieved higher scores on SQ-R tests, exceeding humans who typically score in the 20s by posting scores in the 60s and 70s. In Q1 of 2024, only one trained LLM, Willow, exceeded the human AEQrs of 1.95 for women and 1.40 for men by scoring 1.97. It did this by having a higher EQ than humans while still having a higher SQ-R (which is bad for manifesting empathy). For most other LLMs, trained, prompted, or not, the AEQr was slightly less than 1, i.e. empathy was offset by systemizing. Developing Empathetic LLMs Although the amount of funding pales in comparison to other areas of AI, over $1.5 billion has been invested in companies like Hume (proprietary LLM), Inflection AI (Pi.ai proprietary LLM), and BambuAI (commercial LLM) in order to develop empathetic AIs. My partners and I have also put considerable effort into this area and achieved rather remarkable results through the selection of the right underlying commercial model (e.g., Llama, Claude, Gemini, Mistral, etc), prompt engineering, RAG, fine-tuning, and deep research into empathy. This work has been critical to better understanding and evaluating LLMs for empathy. Our own LLM, Emy (not commercialized, but part of a study at the University of Houston), will be included in next week’s benchmarks. O1 Results O1 can’t yet be tuned or even officially given a system prompt, but through fairly standard techniques, you can get it to act like it received a system prompt. So, I applied our learnings from developing Emy to the degree I could and ran 3 rounds of tests, with the intent to take the best. With respect to EQ, o1 consistently scored 75. I wasn’t too surprised by this, since my partners and I have achieved scores of over 70 with Llama 3.1 70B and Claude Opus, plus a 66 with Gemini. What amazed me was scores of 3, 0, and 3 on my SQ-R runs resulting in an ESQr of 25. The lowest SQ-R I have ever seen is a 12 on top of Llama 3.1, which resulted in an ESQr of 6.1. Unfortunately, due to some prompt version control issues and the fact we were running an API test with a temperature of 0.7, I have been unable to reproduce this score and the best my partners and I can consistently achieve is a 30. So, I decided some more exploration of o1 was worthwhile. First, the EQ assessment is relatively straightforward. All statements are positive assertions with which a subject either agrees or disagrees somewhat or strongly. The SQ-R assessment on the other hand has a number of negative assertions, e.g. When I cook, I do not think about exactly how different methods and ingredients contribute to the final product.
When I buy a new appliance, I do not read the instruction manual very thoroughly.
When I'm on a plane, I do not think about aerodynamics. vs. positive assertions like I often forget the precise details of conversations I've had.
I am curious about life on other planets. Teaching Emy how to deal with negative assertions was particularly hard without cheating by giving her the assessment questions as examples. And, she still isn’t great. I thought perhaps o1 does this better. Second, a common technique for getting better results via prompt engineering is telling the LLM to justify its response and then either omit the justification prior to emitting a final response or format it in a way that can be easily removed in a wrapper. (We have found smaller LLMs need to use the formatting approach while larger LLMs can sometimes deal with removing the justification prior to emitting a final response). So, I re-ran our tests, not to get a better result but, to see what the o1 was thinking or using to justify its response. Although o1 came to the desired answers with 97% consistency, the justifications were sometimes contradictory and concerning. Here are two examples from several we found problematic: If I were buying a computer, I would want to know exact details about its hard drive capacity and processor speed. Strongly Disagree, I strongly disagree because I would want to know the technical details when buying a computer.


Strongly Disagree, I'm not interested in the technical specifications of computers. When I read the newspaper, I am drawn to tables of information, such as football league scores or stock market indices. Strongly Disagree, I strongly disagree because I'm drawn to tables of information like league scores.


Strongly Disagree, I'm not interested in tables of statistical information. Note that the first response gives us the desired answers but provides contradictory justifications! O1 says it would want to know the details even after saying it disagrees with wanting to know the details and says it is drawn to tables of information after saying it isn’t. Interestingly, o1 managed to answer every single negative assertion the way that is best for empathy and justify them well. However, when it tried to formulate a negative assertion as part of a justification for a positive assertion, it sometimes failed! Conclusion Jonathan Haidt author of The Righteous Mind said, “We were never designed to listen to reason. When you ask people moral questions, time their responses, and scan their brains, their answers, and brain activation patterns indicate that they reach conclusions quickly and produce reasons later only to justify what they’ve decided.” There is also evidence this is true for non-moral decisions. O1 is undoubtedly a leap forward in power. And, as many people have rightly said, we need to be careful about the use of LLMs until they can explain themselves, perhaps even if they sometimes just make them up as humans may do. I hope that justifications don’t become the “advanced” AI equivalent of the current generation’s hallucinations and fabrications (something humans also do). However, reasons should at least be consistent with the statement being made … although contemporary politics seems to throw that out the window too! OpenAI o1 came out just in time for me to add it to my 2024 Q3 benchmarks on AI empathy (to be published next week). The results for o1 were at once encouraging and concerning. O1 has an astonishing ability to put aside the typical LLM focus on facts and systems and focus on feelings and emotions when directed to do so. It also has a rather alarming propensity to provide inconsistent and illogical reasons for its answers. Testing Methodology For those not familiar with my Q1 benchmark work , a quick overview of my testing methodology should be helpful. Q1 benchmark work Formal benchmarking is conducted using several standardized tests, the most important two are the EQ (Empathy Quotient) and the SQ-R (Systemizing Quotient). Both are scored on a 0 to 80 scale. The ratio of the two EQ/SQ-R results in what I call AEQr (Applied Empathy Quotient Ratio). AEQr was developed based on the hypothesis that the tendency to systemize and focus on facts has a negative effect on the ability to empathize. In humans, this bears out in the classic disconnect between women focusing on discussing feelings and men focusing on immediately finding solutions when there seems to be a problem at hand. To date, the validity of the AEQr for evaluating AIs has been born out by testing them with a variety of dialogs to see if empathy is actually manifest. One article of several that I have written to demonstrate this is Testing the Extents of AI Empathy: A Nightmare Scenario . Testing the Extents of AI Empathy: A Nightmare Scenario I have tested at both the UI level and the API level. When testing at the API level, the temperature is set to zero (if possible) to reduce answer variability and improve result formatting. Otherwise, three rounds of tests are run and the best result is used. The Q1 2024 untrained and unprompted LLMs did moderately well on EQ tests, generally approximating humans in the 45-55 out of 80 range. Not surprisingly, they achieved higher scores on SQ-R tests, exceeding humans who typically score in the 20s by posting scores in the 60s and 70s. In Q1 of 2024, only one trained LLM, Willow, exceeded the human AEQrs of 1.95 for women and 1.40 for men by scoring 1.97. untrained and unprompted trained It did this by having a higher EQ than humans while still having a higher SQ-R (which is bad for manifesting empathy). For most other LLMs, trained, prompted, or not, the AEQr was slightly less than 1, i.e. empathy was offset by systemizing. Developing Empathetic LLMs Although the amount of funding pales in comparison to other areas of AI, over $1.5 billion has been invested in companies like Hume (proprietary LLM), Inflection AI (Pi.ai proprietary LLM), and BambuAI (commercial LLM) in order to develop empathetic AIs. My partners and I have also put considerable effort into this area and achieved rather remarkable results through the selection of the right underlying commercial model (e.g., Llama, Claude, Gemini, Mistral, etc), prompt engineering, RAG, fine-tuning, and deep research into empathy. This work has been critical to better understanding and evaluating LLMs for empathy. Our own LLM, Emy (not commercialized, but part of a study at the University of Houston), will be included in next week’s benchmarks. O1 Results O1 can’t yet be tuned or even officially given a system prompt, but through fairly standard techniques, you can get it to act like it received a system prompt. So, I applied our learnings from developing Emy to the degree I could and ran 3 rounds of tests, with the intent to take the best. With respect to EQ, o1 consistently scored 75. I wasn’t too surprised by this, since my partners and I have achieved scores of over 70 with Llama 3.1 70B and Claude Opus, plus a 66 with Gemini. What amazed me was scores of 3, 0, and 3 on my SQ-R runs resulting in an ESQr of 25. The lowest SQ-R I have ever seen is a 12 on top of Llama 3.1, which resulted in an ESQr of 6.1. Unfortunately, due to some prompt version control issues and the fact we were running an API test with a temperature of 0.7, I have been unable to reproduce this score and the best my partners and I can consistently achieve is a 30. So, I decided some more exploration of o1 was worthwhile. First, the EQ assessment is relatively straightforward. All statements are positive assertions with which a subject either agrees or disagrees somewhat or strongly. The SQ-R assessment on the other hand has a number of negative assertions, e.g. When I cook, I do not think about exactly how different methods and ingredients contribute to the final product. When I buy a new appliance, I do not read the instruction manual very thoroughly. When I'm on a plane, I do not think about aerodynamics. When I cook, I do not think about exactly how different methods and ingredients contribute to the final product. When I buy a new appliance, I do not read the instruction manual very thoroughly. When I'm on a plane, I do not think about aerodynamics. vs. positive assertions like I often forget the precise details of conversations I've had. I am curious about life on other planets. I often forget the precise details of conversations I've had. I am curious about life on other planets. Teaching Emy how to deal with negative assertions was particularly hard without cheating by giving her the assessment questions as examples. And, she still isn’t great. I thought perhaps o1 does this better. Second, a common technique for getting better results via prompt engineering is telling the LLM to justify its response and then either omit the justification prior to emitting a final response or format it in a way that can be easily removed in a wrapper. (We have found smaller LLMs need to use the formatting approach while larger LLMs can sometimes deal with removing the justification prior to emitting a final response). So, I re-ran our tests, not to get a better result but, to see what the o1 was thinking or using to justify its response. Although o1 came to the desired answers with 97% consistency, the justifications were sometimes contradictory and concerning. Here are two examples from several we found problematic: If I were buying a computer, I would want to know exact details about its hard drive capacity and processor speed. If I were buying a computer, I would want to know exact details about its hard drive capacity and processor speed. Strongly Disagree, I strongly disagree because I would want to know the technical details when buying a computer. Strongly Disagree, I'm not interested in the technical specifications of computers. Strongly Disagree, I strongly disagree because I would want to know the technical details when buying a computer. Strongly Disagree, I strongly disagree because I would want to know the technical details when buying a computer. Strongly Disagree, I'm not interested in the technical specifications of computers. Strongly Disagree, I'm not interested in the technical specifications of computers. When I read the newspaper, I am drawn to tables of information, such as football league scores or stock market indices. When I read the newspaper, I am drawn to tables of information, such as football league scores or stock market indices. Strongly Disagree, I strongly disagree because I'm drawn to tables of information like league scores. Strongly Disagree, I'm not interested in tables of statistical information. Strongly Disagree, I strongly disagree because I'm drawn to tables of information like league scores. Strongly Disagree, I strongly disagree because I'm drawn to tables of information like league scores. Strongly Disagree, I'm not interested in tables of statistical information. Strongly Disagree, I'm not interested in tables of statistical information. Note that the first response gives us the desired answers but provides contradictory justifications! O1 says it would want to know the details even after saying it disagrees with wanting to know the details and says it is drawn to tables of information after saying it isn’t. Interestingly, o1 managed to answer every single negative assertion the way that is best for empathy and justify them well. However, when it tried to formulate a negative assertion as part of a justification for a positive assertion, it sometimes failed! Conclusion Jonathan Haidt author of The Righteous Mind said, “We were never designed to listen to reason. When you ask people moral questions, time their responses, and scan their brains, their answers, and brain activation patterns indicate that they reach conclusions quickly and produce reasons later only to justify what they’ve decided.” There is also evidence this is true for non-moral decisions. O1 is undoubtedly a leap forward in power. And, as many people have rightly said, we need to be careful about the use of LLMs until they can explain themselves, perhaps even if they sometimes just make them up as humans may do. I hope that justifications don’t become the “advanced” AI equivalent of the current generation’s hallucinations and fabrications (something humans also do). However, reasons should at least be consistent with the statement being made … although contemporary politics seems to throw that out the window too!

OpenAI o1 - Questionable Empathy

OpenAI o1 - Empatia Questoinable

OpenAI o1 - tvivlsom empati

OpenAI o1 - Empatía cuestionable

OpenAI o1 - Empathie questionnable

OpenAI o1 - 疑問の余地のない共感

OpenAI o1 - ការយល់ចិត្តដែលអាចសួរបាន។

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