Built an NLP-powered Telegram Bot that parses natural language commands to allow expense-splitting directly in your group chat with fast, secure, on-chain expense records.
Include user adoption rates or feedback to illustrate the bot's effectiveness and popularity.
Developing an AI agent that monitors stablecoin flows in real time and infers intent behind large movements such as panic selling or emerging depeg risks, triggering proactive alerts and automated treasury actions for DAOs and crypto funds.
Clarify the potential financial impact or risk reduction achieved through this AI agent's alerts.
Built a Discord bot to streamline collaborative resume reviews, driving fast and iterative resume improvements for a community of 2000+ students.
Add metrics on how many resumes were improved or user satisfaction ratings to demonstrate impact.
Participated in daily scrum meetings with a team of 5 developers to discuss new ideas and strategies in line with the agile workflow.
Highlight a specific contribution or idea that led to a significant improvement in team performance.
Redesigned layout and fixed critical responsiveness issues on 10+ web pages using Bootstrap, restoring broken mobile views and ensuring consistent, functional interfaces across devices.
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Developed dashboards for an internal portal with .NET Core MVC, eliminating the need for 100+ complex spreadsheets and enabling 30+ executives to securely access operational, financial, and customer data.
Quantify the decision-making improvements or time saved for executives due to the dashboards.
Built a React/.NET impersonation tool enabling admins to emulate employee sessions for support and troubleshooting, cutting developer testing setup time by 86% by eliminating the need for test accounts.
Consider rephrasing to emphasize how this tool improved support response times or user experience.
Led backend unit testing automation for the shift bidding platform using xUnit, SQLite, and Azure Pipelines, contributing 40+ tests, identifying logic errors, and increasing overall coverage by 15%.
Add a specific example of a critical bug found to highlight the importance of your contributions.
Developed an end-to-end shift bid publishing feature using Azure Functions (C#), SQL, and Azure Logic Apps, automating shift imports into the HR system for 700+ employees and saving 50+ hr/month of manual entry.
Clarify the impact by stating how this improved efficiency or employee satisfaction beyond just time saved.
Participated in daily scrum meetings with a team of 5 developers
Highlight any specific contributions or outcomes from these meetings to showcase teamwork impact.
Developed a full-stack web application to help students locate nearby study spots
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triggering proactive alerts and automated treasury actions
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reducing hosting costs by over 90%
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driving fast and iterative resume improvements for a community of 2000+ students.
Mention specific metrics, like the number of resumes improved or user engagement rates.
ensuring consistent, functional interfaces across devices.
Be more specific about the user experience improvements or feedback received post-redesign.
eliminating the need for 100+ complex spreadsheets
Quantify the time or efficiency gained by eliminating these spreadsheets to highlight the impact.
cutting developer testing setup time by 86%
Include the previous setup time for context; this enhances the impact of the achievement.
increasing overall coverage by 15%.
Clarify what 'coverage' refers to—unit tests, code coverage, etc.—for better understanding.
saving 50+ hr/month of manual entry.
Specify the impact of time saved on overall productivity or cost savings for the company.
Developed a full-stack web application to help students locate nearby study spots, track study sessions, and create study groups.
Mention any user adoption rates or feedback to highlight the application's success and relevance.
Built an NLP-powered Telegram Bot that parses natural language commands to allow expense-splitting directly in your group chat.
Consider adding user engagement metrics or feedback to demonstrate the bot's effectiveness.
Developing an AI agent that monitors stablecoin flows in real time and infers intent behind large movements.
Clarify the potential impact of this AI agent on decision-making or risk management for stakeholders.
reducing hosting costs by over 90% and enabling on-demand execution.
Consider specifying the previous hosting costs to give a clearer picture of the savings achieved.
driving fast and iterative resume improvements for a community of 2000+ students.
Include metrics on how many resumes were improved or the average improvement rate to show effectiveness.
Participated in daily scrum meetings with a team of 5 developers to discuss new ideas and strategies in line with the agile workflow.
Highlight any specific contributions or outcomes from these meetings to demonstrate leadership.
restoring broken mobile views and ensuring consistent, functional interfaces across devices.
Use active verbs like 'improved' or 'enhanced' to convey a stronger impact.
eliminating the need for 100+ complex spreadsheets and enabling 30+ executives to securely access operational, financial, and customer data.
Quantify the time saved for executives to highlight the efficiency gained through your work.
cutting developer testing setup time by 86% by eliminating the need for test accounts.
Clarify how this time saving directly benefits project timelines or resource allocation.
increasing overall coverage by 15%.
Specify the baseline coverage percentage to give context to the improvement achieved.
automating shift imports into the HR system for 700+ employees and saving 50+ hr/month of manual entry.
Consider rephrasing to emphasize the impact of the automation on employee productivity and morale.
Developed a full-stack web application to help students locate nearby study spots
🔧 Mention any user feedback or metrics that demonstrate the app's success.
Built an NLP-powered Telegram Bot that parses natural language commands
🔧 Specify how this bot improved user experience or efficiency.
Developing an AI agent that monitors stablecoin flows in real time
🔧 Clarify the potential business impact or use case for this AI agent.
Languages : Java, Python, C#, C, JavaScript, TypeScript, HTML/CSS, PHP , SQL(Postgres, MySQL, MSSQL)
📝 Remove extra space before the comma after PHP and ensure consistent spacing throughout.
•Built a Discord bot to streamline collaborative resume reviews
💪 Great initiative; consider mentioning any user engagement metrics to showcase success.
•Developed a full-stack web application to help students locate nearby study spots
💡 Include user metrics or feedback to demonstrate the app's effectiveness or popularity.
Developed dashboards for an internal portal with .NET Core MVC
🔧 Specify the impact of dashboards on decision-making or efficiency to enhance this bullet.
Technical Skills
💪 Comprehensive skill set; consider prioritizing skills relevant to the job you are applying for.
AWS Certified Cloud Practitioner
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ResuRalph |Python, Flask, Docker, AWS(Lambda, DynamoDB, S3) |GitHub
📝 Add spaces after commas for consistency in project formatting (e.g., AWS (Lambda, DynamoDB, S3)).
Participated in daily scrum meetings with a team of 5 developers
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cutting developer testing setup time by 86%
💪 Excellent use of metrics to showcase efficiency gains; maintain this focus on results.
saving 50+ hr/month of manual entry.
💪 Quantified achievement effectively demonstrates impact; continue this practice throughout.
Bachelor of Computer Science (Co-op) (GPA:4.16/4.3)
💪 Strong GPA reflects academic excellence; consider adding relevant coursework to highlight skills.
Developed dashboards for an internal portal with .NET Core MVC
🔧 Add specific technologies used or methodologies applied to enhance technical depth.
Built a Discord bot to streamline collaborative resume reviews
💪 Unique project showcases initiative and technical skills; ensure all projects highlight similar impact.
AWS Certified Cloud Practitioner
💪 Certification adds credibility; consider adding more certifications or relevant training if available.
Relevant Courses: Data Structures and Algorithms, Database Systems, Software Development
💡 Consider listing courses relevant to desired job roles to enhance alignment with job descriptions.
Languages : Java, Python, C#, C, JavaScript, TypeScript, HTML/CSS, PHP , SQL(Postgres, MySQL, MSSQL)
📝 Remove extra spaces and ensure consistent punctuation for a cleaner look.
Implemented in-line PDF annotations through integration with Hypothes.is
🔧 Clarify the impact of this feature on user experience or efficiency for better context.
Participated in daily scrum meetings with a team of 5 developers
💡 Consider emphasizing specific contributions or outcomes from scrum meetings to highlight teamwork.
cutting developer testing setup time by 86%
💪 Excellent use of metrics to illustrate efficiency improvements; maintain this level of detail.
saving 50+ hr/month of manual entry.
💪 Quantified achievement effectively showcases impact; continue this trend in all experience descriptions.
Bachelor of Computer Science (Co-op) (GPA:4.16/4.3)
💪 Strong GPA demonstrates academic excellence; consider adding relevant coursework to highlight skills.
AI and RPA are reshaping how businesses operate by combining automation with intelligence. This blog explores their individual roles, synergy, real-world use cases, and how they drive smarter, faster, and more scalable business processes.
Discover how AI and RPA revolutionize business operations by automating workflows, reducing costs, enhancing accuracy, and driving digital transformation across industries.
Oh yeah. If you're generating text that could burn anywhere from 0.17 watt hours to 2 watt hours, equal to running this grill for about four seconds. Generating an image add 1.7 watt hours. All that, less than 10 seconds on the grill. But short videos can use far more power. In tests of various open source models, videos took anywhere between 20 watt hours and 110 watt hours. At 110 watt hours, one steamed electric grill steak, about equal to one video generation. I wouldn't eat it, but my dog would. At 220 watt hours, it was looking much more edible. So two video generations equals one pretty good looking steak.
https://web.archive.org/web/20250719082633/https://paulkedrosky.com/honey-ai-capex-ate-the-economy/ by Paul Kedrosky On the enormous capital expenditures for AI related data centers, at the level of 2% US GDP. A volume big enough to influence overall economy / hide other currents in economy.
Navigating Failures in Pods With Devices
This article examines the unique challenges Kubernetes faces in managing specialized hardware (e.g., GPUs, accelerators) within AI/ML workloads, and explores current pain points, DIY solutions, and the future roadmap for more robust device failure handling.
Kubernetes Infrastructure Failures
Device Failures
Container Code Failures
Device Degradation
SIG Node and Kubernetes community are focusing on:
restartPolicy: Always.Kubernetes remains the platform of choice for AI/ML, but device- and hardware-aware failure handling is still evolving. Most robust solutions are still "DIY," but community and upstream investment is underway to standardize and automate recovery and resilience for workloads depending on specialized hardware.
Businesses are rapidly relying on artificial intelligence to enhance user experiences, automate processes, and gain competitive advantages. However, understanding AI app development cost remains one of the biggest challenges for companies planning to create artificial intelligence app solutions.
Explore the key factors influencing AI app development cost in 2025. Learn how app complexity, features, and tech stack impact your budget for smart AI solutions.
C=EP-V Cheating equation and AI discussion
What is an agent? read more in detail
Virtual shopping agent technology combines natural language processing with predictive analytics. This combination allows such systems to understand customer queries in natural languages while predicting future purchasing behaviour. The result is a more intuitive and efficient shopping experience that feels personal and responsive. Intelligent shopping bots represent the most advanced form of these systems. They can handle complex customer interactions, process multiple data points simultaneously, and learn from every interaction.
Explore how AI agents for online shopping revolutionize with personalization, automation, and smart recommendations reshaping the future of eCommerce.
Automating oral argument
A Harvard Law graduate who argued before the Supreme Court fed his case briefs into Claude 4 Opus and had it answer the same questions the Justices posed to him. The AI delivered what he called an "outstanding oral argument" with coherent answers and clever responses he hadn't considered, leading him to conclude that AI lawyers could soon outperform even top human advocates at oral argument.
Inter-node communication stalls: high batching is crucial to profitably serve millions of users, and in the context of SOTA reasoning models, many nodes are often required. Inference workloads then resemble more training.
Oh, so to get the highest throughout, the inference servers also batch operations making it look a bit like training too
In today’s fast-moving, AI-powered era, autonomous agents are playing a bigger role than ever. They are helping businesses run smoother and making decisions affecting millions of lives every day. While these systems are designed to make our lives easier and unlock new opportunities, we can’t get carried away—we need to implement proper AI Agent Evaluation frameworks and best practices to ensure these systems actually work as intended and follow ethical AI principles.
Explore the key metrics, tools, and frameworks used for AI agent evaluation. Learn how to assess performance, reliability, and efficiency of AI agents in real-world scenarios.
Most businesses are making the jump from traditional, reactive and static applications to intelligent, proactive Flutter applications that understand and analyze user behaviour, and adapt accordingly. Moreover, 71% of consumers show interest in wanting Gen AI integrations for their shopping applications.
Learn how to integrate AI into Flutter apps to deliver smarter, more intuitive mobile experiences. Discover tools, techniques, and best practices for Flutter AI integration.
'agent washing' Agentic AI underperforms, getting at most 30% tasks right (Gemini 2.5-Pro) but mostly under 10%.
Article contains examples of what I think we should agentic hallucination, where not finding a solution, it takes steps to alter reality to fit the solution (e.g. renaming a user so it was the right user to send a message to, as the right user could not be found). Meredith Witthaker is mentioned, but from her statement I saw a key element is missing: most of that access will be in clear text, as models can't do encryption. Meaning not just the model, but the fact of access existing is a major vulnerability.
Browser-based applications operate entirely within web browsers using standard technologies like HTML, CSS, and JavaScript. Unlike desktop applications requiring local installation, these applications run through web browsers and access device capabilities through modern web APIs. This approach enables cross-platform compatibility and immediate accessibility from any internet-connected device.
Learn how to build a browser-based AI application with step-by-step insights on tools, frameworks, and best practices. Explore scalable solutions for real-time AI in the browser.
2) Why do they only use 1 GPU, no servers, and a short training time in the embodied carbon estimates
LOL, do people really use GPU's without any surrounding infrastructure?
for - post LinkedIn - book - From Bacteria to AI - reminds me of Micheal Levin's cognitive light cones - adjacency - Micheal Levin's - Katherine Hayle - cognition
totally AI-generated resumes have a sameness to them that recruiters can tell right away. They all use similar language, and they’re almost identical.
what could AI do with everything I’ve shared? Could it blackmail me? Sell the information? Use it to manipulate me? Get me to buy something, vote a certain way, believe a certain story?
for - progress trap - AI - sharing intimate details with
By "augmenting human intellect" we mean increasing the capability of a man to approach a complex problem situation, to gain comprehension to suit his particular needs, and to derive solutions to problems.
Pulling back to this post - https://wiobyrne.com/embracing-the-violin-of-cognitive-amplification/
2019 WSJ report on the vaporware Build.ai. Folded 6 yrs later. Meaning investors turned a blind eye for all that time.
March 2022 Techcrunch article already pointing to the vapor aspect of Builder.ai which folded in June 2025. Points back to 2019 reports where the same issue was already raised. Meaning later investors were wilfully blind for 6 yrs.
'It turns out the company had no AI and instead was just a group of Indian developers pretending to write code as AI,
'AI' softw dev company, is actually a pool of 700 India based coders. Exposed because they couldn't meet payroll....
1000x Increase in AI Demand
advanced AI (but not “superintelligent” AI,
wish there was a clear cut definition or at least advertisement of authors' stakes, stances, and definitions of the following terms
technological determinism; agent; intelligence; control; progress; alignment
The answer most technocrats are leaning towards is vector search technology and Retrieval-Augmented Generation (RAG) models that improve AI experiences. These intelligent search systems are fundamentally changing how users discover information, interact with applications, and receive personalized experiences across industries.
Explore how embedding intelligence transforms Vector Search and RAG (Retrieval-Augmented Generation) models. Learn the key benefits, use cases, and implementation strategies for smarter AI-driven search systems.
Anthropic researchers said this was not an isolated incident, and that Claude had a tendency to “bulk-email media and law-enforcement figures to surface evidence of wrongdoing.”
for - question - progress trap - open source AI models - for blackmail and ransom - Could a bad actor take an open source codebase and twist it to do harm like find out about an rogue AI creator's adversary, enemy or victim and blackmail them? - progress trap - open source AI - criminals - exploit to identify and blackmail victiims
for - progress trap - AI - Anthropic Claude 4 - blackmail - from - youtube - Kyle Kilinski Show - AI is completely out of control - https://hyp.is/GhDOzj0nEfCvHZdiUaw4gQ/www.youtube.com/watch?v=4j1gjSoRt8Q
The researchers called the behavior “rare” and “difficult to elicit.
for - progress trap - AI - Anthropic Claude 4 - blackmail - rare behavior - but still possible! It only has to happen once!
anthropic's new AI model shows ability to deceive and blackmail
for - progress trap - AI - blackmail - AI - autonomy - progress trap - AI - Anthropic - Claude Opus 4 - to - article - Anthropic Claude 4 blackmail and news leak - progress trap - AI - article - Anthropic Claude 4 - blackmail - rare behavior - Anthropic’s new AI model didn’t just “blackmail” researchers in tests — it tried to leak information to news outlets
for - progress trap - AI - AI - blackmailing human creators - AI - autonomy
we saw this with Grock uh basically rebelling against Elon Musk
for - progress trap - AI - autonomy - example - Grok
An IBM survey of 2,000 CEOs revealed that just 25% of AI projects deliver on their promised return on investment. The main driver of adoption, it seems, is corporate FOMO, with nearly two-thirds of CEOs agreeing that “the risk of falling behind drives them to invest in some technologies before they have a clear understanding of the value they bring to the organization,” according to the study.
New stat from IBM? This is similar to the RAND figure from before?
AI skin analysis technology provides deeply personalized customer journeys that traditional approaches simply cannot recreate. With its ability to analyze various skin parameters at the same time, Haut.AI is able to identify specific concerns and recommend targeted products or treatment processes.
Unlock smarter beauty tech with Haut.AI integration services. From AI skin analysis to AI dermatology technology and skin condition detection, empower your health and beauty app with personalized, data-driven skincare insights. Partner with CMARIX to lead in AI-powered wellness solutions.
for - progress trap - AI - Grok - Elon Musk programs Grok to lie about South African refuges
Most legacy apps that aren’t putting efforts into modernization or AI integration are either breaking even, or nearing their demise due to the inability to deliver personalized experiences and use data-driven insights that define market leaders in artificial intelligence legacy systems implementations.
Integrating modern technologies into outdated infrastructures doesn't have to be a challenge. Discover how businesses are successfully integrating AI into legacy systems with NET Core to boost performance, enable predictive insights, and stay ahead in the competitive digital world.
From enhancing data processing to automating workflows, AI and .NET Core offer the perfect synergy to modernize applications without a complete rebuild. 💡
for - natural language acquisition - Automatic Language Growth - ALG - youtube - interview - David Long - Automatic Language Growth - from - youtube - The Language School that Teaches Adults like Babies - https://hyp.is/Ls_IbCpbEfCEqEfjBlJ8hw/www.youtube.com/watch?v=984rkMbvp-w
summary - The key takeaway is that even as adults, we have retained our innate language learning skill which requires simply treating a new language as a new, novel experience that we can apprehend naturally simply by experiencing it like the way we did when we were exposed to our first, native language - We didn't know what a "language" was theoretically when we were infants, but we simply fell into the experience and played with the experiences and our primary caretakers guided us - We didn't know grammar and rules of language, we just learned innately
Once multiple accurate students enter the same tag for a new image, the system wouldbe confident that the tag is correct. In this manner, image tagging and vocabulary learning can becombined into a single activity.
is this not how CAPTCHA is evaluated too?
"a man who understands Chinese is not a man who has a firm grasp of the statistical probabilities for the occurrence of the various words in the Chinese language" (p. 108).
cf./viz. classical statistical machine learning and language models
Gottfried Leibniz made a similar argument in 1714 against mechanism (the idea that everything that makes up a human being could, in principle, be explained in mechanical terms. In other words, that a person, including their mind, is merely a very complex machine).
anatomy of a landscape / atrocity exhibition
Microsoft Azure has a dedicated ecosystem of AI tools for streamlining business workflows and building smart web applications. With tools like Azure Cognitive Services, Azure Bot Services in enterprise apps and Azure Machine Learning, businesses can get a one-stop-solution for all their AI requirements without having to rely on multiple vendors.
Unlock next-gen business growth by building AI-powered enterprise applications using Microsoft Azure. Discover how Azure AI services, machine learning, and scalable cloud infrastructure empower businesses to streamline operations, drive automation, and make data-driven decisions with confidence.
for - report - America's Superintelligence Project - definition - ASI - Artificial Super Intelligence
summary - What is the cost of mistrust between nation states? - The mistrust between the US and China is reaching an all-tie high and it has disastrous consequences for an AI arms race - It is driving each country to move fast and break things, which will become an existential threat to all humanity - Deep Humanity, with an important dimension of progress traps can help us navigate ASI
AI containment
for - definition - AI containment - progress trap - AI containment
example
for - example - AI unpredicted behavior
To this day, if you know the right people, the Silicon Valley gossip mill is a surprisingly reliable source of information if you want to anticipate the next beat in frontier AI – and that’s a problem. You can’t have your most critical national security technology built in labs that are almost certainly CCP-penetrated
for - high security risk - US AI labs
AI is already augmenting important parts of the AI research process itself, and that will only accelerate
for - quote - AI - AI is accelerating AI research itself
at any given time, the CCP may have a better idea of what OpenAI’s frontier advances look like than the U.S. government does.
for - AI - Chinese know more than US government about latest US frontier AI research
https://web.archive.org/web/20250423134653/https://www.rijksoverheid.nl/documenten/publicaties/2025/04/22/het-overheidsbrede-standpunt-voor-de-inzet-van-generatieve-ai Rijksstandpunt genAI, mede gebaseerd op IEC advies IPO. Niettemin wordt het hier lijkt me behoorlijk vrij gegeven, en de formulering klinkt heel los. Gaat problemen opleveren, want een bmw die met genAI speelt bij het opstellen van een stuk het voor zichzelf als 'experiment' labelt of 'innovatie' heeft het voor zich daarmee gerationaliseerd. Never mind dat experimenten gecontroleerde omstandigheden vergen, en innovatie een gedeelde intentie moet hebben in de org. Dit voelt heel zacht aan, staan de juiste dingen in desondanks
[[When Will the GenAI Bubble Burst]]
misled investors by exploiting the promise and allure of AI technology to build a false narrative about innovation that never existed. This type of deception not only victimizes innocent investors
The crime was misleading investors, not anyone else, which is very telling. The hype around "AI" - and actually hiring remote workers to do the job - and misleading customers/users doesn't matter.
In truth, nate relied heavily on teams of human workers—primarily located overseas—to manually process transactions in secret, mimicking what users believed was being done by automation
Yet another example of "AI" being neither artificial nor intelligent.
This change means many data centers built in central, western, and rural China—where electricity and land are cheaper—are losing their allure to AI companies. In Zhengzhou, a city in Li’s home province of Henan, a newly built data center is even distributing free computing vouchers to local tech firms but still struggles to attract clients.
Interesting cautionary tale about building out DCs in the styx, where energy is cheap but latency is high
Instead of drafting a first version with pen and paper (my preferred writing tools), I spent an entire hour walking outside, talking to ChatGPT in Advanced Voice Mode. We went through all the fuzzy ideas in my head, clarified and organized them, explored some additional talking points, and eventually pulled everything together into a first outline.
Need to try this out.
Review coordinated by Life Science Editors Foundation Reviewed by: Dr. Angela Andersen, Life Science Editors Foundation & Life Science Editors Potential Conflicts of Interest: None
PUNCHLINE Evo 2 is a biological foundation model trained on 9.3 trillion DNA bases across all domains of life. It predicts the impact of genetic variation—including in noncoding and clinically relevant regions—without requiring task-specific fine-tuning. Evo 2 also generates genome-scale sequences and epigenomic architectures guided by predictive models. By interpreting its internal representations using sparse autoencoders, the model is shown to rediscover known biological features and uncover previously unannotated patterns with potential functional significance. These capabilities establish Evo 2 as a generalist model for prediction, annotation, and biological design.
BACKGROUND A foundation model is a large-scale machine learning model trained on massive and diverse datasets to learn general features that can be reused across tasks. Evo 2 is such a model for genomics: it learns from raw DNA sequence alone—across bacteria, archaea, eukaryotes, and bacteriophage—without explicit labels or training on specific tasks. This enables it to generalize to a wide range of biological questions, including predicting the effects of genetic variants, identifying regulatory elements, and generating genome-scale sequences or chromatin features.
Evo 2 comes in two versions: one with 7 billion parameters (7B) and a larger version with 40 billion parameters (40B). These numbers reflect the number of trainable weights in the model and influence its capacity to learn complex patterns. Both models were trained using a context window of up to 1 million tokens—where each token is a nucleotide—allowing the model to capture long-range dependencies across entire genomic regions.
Evo 2 learns via self-supervised learning, a method in which the model learns to predict masked or missing DNA bases in a sequence. Through this simple but powerful objective, the model discovers statistical patterns that correspond to biological structure and function, without being told what those patterns mean.
QUESTION ADDRESSED Can a large-scale foundation model trained solely on genomic sequences generalize across biological tasks—such as predicting mutational effects, modeling gene regulation, and generating realistic genomic sequences—without supervision or task-specific tuning?
SUMMARY The authors introduce Evo 2, a foundational model for genomics that generalizes across DNA, RNA, and protein tasks. Without seeing any biological labels, Evo 2 learns the sequence rules governing coding and noncoding function, predicts variant effects—including in BRCA1/2 and splicing regions—and generates full-length genomes and epigenome profiles. It also enables epigenome-aware sequence design by coupling sequence generation with predictive models of chromatin accessibility.
To probe what the model has learned internally, the authors use sparse autoencoders (SAEs)—a technique that compresses the model’s internal activations into a smaller set of interpretable features. These features often correspond to known biological elements, but importantly, some appear to capture novel, uncharacterized patterns that do not match existing annotations but are consistently associated with genomic regions of potential functional importance. This combination of rediscovery and novelty makes Evo 2 a uniquely powerful tool for exploring both the known and the unknown genome.
KEY RESULTS Evo 2 trains on vast genomic data using a novel architecture to handle long DNA sequences Figures 1 + S1 Goal: Build a model capable of representing entire genomic regions (up to 1 million bases) from any organism. Outcome: Evo 2 was trained on 9.3 trillion bases using a hybrid convolution-attention architecture (StripedHyena 2). The model achieves long-context recall and strong perplexity scaling with increasing sequence length and model size.
Evo 2 predicts the impact of mutations across DNA, RNA, and protein fitness Figures 2A–J + S2–S3 Goal: Assess whether Evo 2 can identify deleterious mutations without supervision across diverse organisms and molecules. Outcome: Evo 2 assigns lower likelihoods to biologically disruptive mutations—e.g., frameshifts, premature stops, and non-synonymous changes—mirroring evolutionary constraint. Predictions correlate with deep mutational scanning data and gene essentiality assays. Evo 2 embeddings also support highly accurate exon-intron classifiers.
Clarification: “Generalist performance across DNA, RNA, and protein tasks” means that Evo 2 can simultaneously make accurate predictions about the functional impact of genetic variants on transcription, splicing, RNA stability, translation, and protein structure—without being specifically trained on any of these tasks.
Evo 2 achieves state-of-the-art performance in clinical variant effect prediction Figures 3A–I + S4 Goal: Evaluate Evo 2's ability to predict pathogenicity of human genetic variants. Outcome: Evo 2 matches or outperforms specialized models on coding, noncoding, splicing, and indel variants. It accurately classifies BRCA1/2 mutations and generalizes to novel variant types. When paired with supervised classifiers using its embeddings, it achieves state-of-the-art accuracy on BRCA1 variant interpretation.
Evo 2 representations reveal both known and novel biological features through sparse autoencoders Figures 4A–G + S5–S7 Goal: Understand what Evo 2 has learned internally. Outcome: Sparse autoencoders decompose Evo 2’s internal representations into distinct features—many of which align with well-known biological elements such as exon-intron boundaries, transcription factor motifs, protein secondary structure, CRISPR spacers, and mobile elements. Importantly, a subset of features do not correspond to any known annotations, yet appear repeatedly in biologically plausible contexts. These unannotated features may represent novel regulatory sequences, structural motifs, or other functional elements that remain to be characterized experimentally.
Note: Sparse autoencoders are neural networks that reduce high-dimensional representations to a smaller set of features, enforcing sparsity so that each feature ideally captures a distinct biological signal. This approach enables mechanistic insight into what the model “knows” about sequence biology.
Evo 2 generates genome-scale sequences with realistic structure and content Figures 5A–L + S8 Goal: Assess whether Evo 2 can generate complete genome sequences that resemble natural ones. Outcome: Evo 2 successfully generates mitochondrial genomes, minimal bacterial genomes, and yeast chromosomes. These sequences contain realistic coding regions, tRNAs, promoters, and structural features. Predicted proteins fold correctly and recapitulate functional domains.
Evo 2 enables design of DNA with targeted epigenomic features Figures 6A–G + S9 Goal: Use Evo 2 to generate DNA sequences with user-defined chromatin accessibility profiles. Outcome: By coupling Evo 2 with predictors like Enformer and Borzoi, the authors guide generation to match desired ATAC-seq profiles. Using a beam search strategy—where the model explores and ranks multiple possible output sequences—it generates synthetic DNA that encodes specific chromatin accessibility patterns, such as writing “EVO2” in open/closed chromatin space.
STRENGTHS First large-scale, open-source biological foundation model trained across all domains of life
Performs well across variant effect prediction, genome annotation, and generative biology
Demonstrates mechanistic interpretability via sparse autoencoders
Learns both known and novel biological features directly from raw sequence
Unsupervised learning generalizes to clinical and functional genomics
Robust evaluation across species, sequence types, and biological scales
FUTURE WORK & EXPERIMENTAL DIRECTIONS Expand training to include viruses that infect eukaryotic hosts: Evo 2 currently excludes these sequences, in part to reduce potential for misuse and due to their unusual nucleotide structure and compact coding. As a result, Evo 2 performs poorly on eukaryotic viral sequence prediction and generation. Including these genomes could expand its applications in virology and public health.
Empirical validation of novel features: Use CRISPR perturbation, reporter assays, or conservation analysis to test Evo 2-derived features that don’t align with existing annotations.
Targeted mutagenesis: Use Evo 2 to identify high-impact or compensatory variants in disease-linked loci, and validate using genome editing or saturation mutagenesis.
Epigenomic editing: Validate Evo 2-designed sequences for chromatin accessibility using ATAC-seq or synthetic enhancer assays.
Clinical applications: Fine-tune Evo 2 embeddings to improve rare disease variant interpretation or personalized genome annotation.
Synthetic evolution: Explore whether Evo 2 can generate synthetic genomes with tunable ecological or evolutionary features, enabling testing of evolutionary hypotheses.
AUTHORSHIP NOTE This review was drafted with support from ChatGPT (OpenAI) to help organize and articulate key ideas clearly and concisely. I provided detailed prompts, interpretations, and edits to ensure the review reflects an expert understanding of the biology and the paper’s contributions. The final version has been reviewed and approved by me.
FINAL TAKEAWAY Evo 2 is a breakthrough in foundation models for biology—offering accurate prediction, functional annotation, and genome-scale generation, all learned from raw DNA sequence. By capturing universal patterns across life, and identifying both well-characterized and unknown sequence features, Evo 2 opens powerful new directions in evolutionary biology, genomics, and biological design. Its open release invites widespread use and innovation across the life sciences.
Or is that the rainbow?
In our paper, we address this idea that ALL ✨sparkling intelligence✨ outputs are generated using the same technology and practices. We argue that it is useful to have a term for those outputs that don't match our shared reality or factual requirements, and for that we propose "mirage".
In my blog post introducing our paper, I suggest AI "rainbows" as a term for mirages that we do value.
I asked our friend Dr. Oblivion, Why is it better to refer to AI hallucinations and AI mirages? His response.
I'm assuming this is some kind of ✨sparkling intelligence✨ and given that Dr. Oblivion seems to miss the point of the paper and our discussion here, I found it more illustrative than helpful ;)
Does anybody know who came up with the term “hallucinations” in the first place? Was it Sutskever?
Turns out the story is a bit more complicated than that, at least according to the history shared by another participant below.
Joshua Pearson examines the history of the term “hallucination” in the development and promotion of AI technology: “Why ‘Hallucination’? Examining the History, and Stakes, of How We Label AI’s Undesirable Output” (2024).
This is a great history of the term "hallucination" in the discourse of ✨sparkling intelligence✨ — huge thanks to whoever shared it! I've also added it to our collaborative bibliography.
推理模型 (deepseek-reasoner) deepseek-reasoner 是 DeepSeek 推出的推理模型。在输出最终回答之前，模型会先输出一段思维链内容，以提升最终答案的准确性。我们的 API 向用户开放 deepseek-reasoner 思维链的内容，以供用户查看、展示、蒸馏使用。 在使用 deepseek-reasoner 时，请先升级 OpenAI SDK 以支持新参数。 pip3 install -U openai API 参数 输入参数： max_tokens：最终回答的最大长度（不含思维链输出），默认为 4K，最大为 8K。请注意，思维链的输出最多可以达到 32K tokens，控思维链的长度的参数（reasoning_effort）将会在近期上线。 输出字段： reasoning_content：思维链内容，与 content 同级，访问方法见访问样例 content：最终回答内容 上下文长度：API 最大支持 64K 上下文，输出的 reasoning_content 长度不计入 64K 上下文长度中 支持的功能：对话补全，对话前缀续写 (Beta) 不支持的功能：Function Call、Json Output、FIM 补全 (Beta) 不支持的参数：temperature、top_p、presence_penalty、frequency_penalty、logprobs、top_logprobs。请注意，为了兼容已有软件，设置 temperature、top_p、presence_penalty、frequency_penalty 参数不会报错，但也不会生效。设置 logprobs、top_logprobs 会报错。 上下文拼接 在每一轮对话过程中，模型会输出思维链内容（reasoning_content）和最终回答（content）。在下一轮对话中，之前轮输出的思维链内容不会被拼接到上下文中，如下图所示： 请注意，如果您在输入的 messages 序列中，传入了reasoning_content，API 会返回 400 错误。因此，请删除 API 响应中的 reasoning_content 字段，再发起 API 请求，方法如访问样例所示。 访问样例 下面的代码以 Python 语言为例，展示了如何访问思维链和最终回答，以及如何在多轮对话中进行上下文拼接。
deepseek推理型 #AI #大模型
The Future of AI & Digital Innovation
for - program event selection - 2025 - April 4 - 10:30am-12pm GMT - Skoll World Forum - The Future of AI & Digital Innovation - Stop Reset Go - Indyweb -- relevant to
Delegate Led Discussion - The Changing State of AI, Media
for - program event selection - 2025 - April 2 - 2-3:15pm GMT - Skoll World Forum - The Changing State of AI, Media - Indyweb - Stop Reset Go - TPF - Eric's project - Skoll's Participatory Media project - relevant to - adjacency - indyweb - Stop Reset Go - participatory news - participatory movie and tv show reviews - Eric's project - Skoll's Particiipatory Media - event time conflict - with - Leadership in Alien Times
adjacency - between - Skoll's Participatory Media project - Global Witness - Indyweb - Stop Reset Go's participatory news idea - Stop Reset Go's participatory movie and TV show review idea - Eric's media project - adjacency relationship - Participatory media via Indyweb and idea of participatory news and participatory movie and tv show reviews - might be good to partner with Skoll Foundation's Participatory Media group
The results indicated that CellProfiler showed good performance across various evaluation metrics
It's fascinating that despite the surge in advanced deep learning methods, traditional non-AI approaches like CellProfiler continue to deliver superior performance in cell segmentation.
before the internet it was impossible really I mean getting coring people into town halls regularly that would have been a hard thing to do anyway online made a bit easier but now with aii we can actually all engage with each other AI can be used to harvest the opinions of millions of people at the same time and distill those opinions into a consensus that might be agreeable to the vast majority
for - claim - AI for a new type of democracy? - progress trap - AI - future democracy
Anshumali's prime research work on SLIDE algorithms.
Put another way, ChatGPT seems so human because it was trained by an AI that was mimicking humans who were rating an AI that was mimicking humans who were pretending to be a better version of an AI that was trained on human writing. This circuitous technique is called “reinforcement learning from human feedback,” or RLHF, and it’s so effective that it’s worth pausing to fully register what it doesn’t do. When annotators teach a model to be accurate, for example, the model isn’t learning to check answers against logic or external sources or about what accuracy as a concept even is. The model is still a text-prediction machine mimicking patterns in human writing, but now its training corpus has been supplemented with bespoke examples, and the model has been weighted to favor them. Maybe this results in the model extracting patterns from the part of its linguistic map labeled as accurate and producing text that happens to align with the truth, but it can also result in it mimicking the confident style and expert jargon of the accurate text while writing things that are totally wrong. There is no guarantee that the text the labelers marked as accurate is in fact accurate, and when it is, there is no guarantee that the model learns the right patterns from it.
RLHF
I have adopted a no-GPT approach here because I believe in smaller open source models. I am using the fantastic Mistral 7B Openorca instruct and Zephyr models. These models can be set up locally with Ollama.
for - open source AI
for - Indyweb dev - open source AI - text to graph - from - search - image - google - AI that converts text into a visual graph - https://hyp.is/KgvS6PmIEe-MjXf4MH6SEw/www.google.com/search?sca_esv=341cca66a365eff2&sxsrf=AHTn8zoosJtp__9BMEtm0tjBeXg5RsHEYA:1741154769127&q=AI+that+converts+text+into+visual+graph&udm=2&fbs=ABzOT_CWdhQLP1FcmU5B0fn3xuWpA-dk4wpBWOGsoR7DG5zJBjLjqIC1CYKD9D-DQAQS3Z598VAVBnbpHrmLO7c8q4i2ZQ3WKhKg1rxAlIRezVxw9ZI3fNkoov5wiKn-GvUteZdk9svexd1aCPnH__Uc8IUgdpyeAhJShdjgtFBxiTTC_0C5wxBAriPcxIadyznLaqGpGzbn_4WepT8N6bRG3HQLK-jPDg&sa=X&ved=2ahUKEwju5oz8ovKLAxW6WkEAHaSVN98QtKgLegQIEhAB&biw=1920&bih=911&dpr=1 - to - example - open source AI - convert text to graph - https://hyp.is/UpySXvmKEe-l2j8bl-F6jg/rahulnyk.github.io/knowledge_graph/
https://rahulnyk.github.io/knowledge_graph/
for - Indyweb dev - text to graph - open source AI - convert text to graph - adjacency - infranodus - to - AI program to convert text into visual graph
for - Indyweb dev - open source AI - text to graph - open source AI - text to graph - from - article - Medium - How to Convert Any Text Into a Graph of Concepts - https://hyp.is/vu53YvmIEe-DuHvXodWFAA/medium.com/towards-data-science/how-to-convert-any-text-into-a-graph-of-concepts-110844f22a1a
for - search - Google - image - AI that converts text into any visual graph - https://www.google.com/search?sca_esv=341cca66a365eff2&sxsrf=AHTn8zoosJtp__9BMEtm0tjBeXg5RsHEYA:1741154769127&q=AI+that+converts+text+into+visual+graph&udm=2&fbs=ABzOT_CWdhQLP1FcmU5B0fn3xuWpA-dk4wpBWOGsoR7DG5zJBjLjqIC1CYKD9D-DQAQS3Z598VAVBnbpHrmLO7c8q4i2ZQ3WKhKg1rxAlIRezVxw9ZI3fNkoov5wiKn-GvUteZdk9svexd1aCPnH__Uc8IUgdpyeAhJShdjgtFBxiTTC_0C5wxBAriPcxIadyznLaqGpGzbn_4WepT8N6bRG3HQLK-jPDg&sa=X&ved=2ahUKEwju5oz8ovKLAxW6WkEAHaSVN98QtKgLegQIEhAB&biw=1920&bih=911&dpr=1
search - google - image - AI that converts text into visual graph - interesting results returned - to - article - Medium - How to convert any text into a graph of concepts -
AI stands poised to reveal its most vital purpose: nurturing thoughtful, capable and intrinsically motivated learners
I wonder how "purpose" is defined here. Is this why LLM applications were developed, or something they were meant to achieve?
For instance, an AI-powered platform might track how many practice problems a student has completed, indicate skills and competencies with which they struggle most, and show how their performance improves over time.
Is this an example of personalization and making AI an ally, or of locking the student into a turbocharged LMS?
0 / 3 Notes free
Se pot crea DOAR 3 note în varianta FREE!
(Cultural Insight Website, n.d.).
Fake citation AI generated
Nate Angell
You might also want to visit my blog post, where I introduce the publication of this paper alongside some additional ideas on interventions to prevent AI mirages, on AI mirages vs AI rainbows, and on how AI terminology plays out in different disciplines.
AI systems examine user behavior and preferences to make personalized recommendations. It uses collaborative filtering or content-based filtering approaches to recommend items, articles, or other relevant material to consumers. These recommendations ASP.NET Core with AI models can be trained and used in your .NET Core web apps.
Leverage the power of AI integration in ASP.NET Core applications to enhance efficiency, automate processes, and improve decision-making. From machine learning in ASP.NET Core to AI models in .NET Core, businesses can unlock intelligent automation, predictive analytics, and real-time data processing. Whether integrating AI in .NET Core for chatbots, recommendation engines, or fraud detection, the possibilities are endless!
tools such as GenAI have begun to lead human actors to increasingly treat technologies as social actors... Humans perceive social cues in technology, which may trigger the (mis)application of interaction scripts learned from human interaction.
for - AI - as extreme human echo chamber - Jonathan Boymal - AI
If robust general-purpose reasoning abilities have emerged in LLMs, this bolsters the claim that such systems are an important step on the way to trustworthy general intelligence.
While large language models (LLMs) are not explicitly trained to reason, they have exhibited “emergent” behaviors that sometimes look like reasoning.
The word “reasoning” is an umbrella term that includes abilities for deduction, induction, abduction, analogy, common sense, and other “rational” or systematic methods for solving problems. Reasoning is often a process that involves composing multiple steps of inference.
LLMs are substantially better at solving problems that involve terms or concepts that appear more frequently in their training data, leading to the hypothesis that LLMs do not perform robust abstract reasoning to solve problems, but instead solve problems (at least in part) by identifying patterns in their training data that match, or are similar to, or are otherwise related to the text of the prompts they are given.
[Memorization and reasoning are] not a dichotomy, but rather they can co-exist in a continuum.
on copyright and artistic style
The startup shut down in 2021, citing the cost of litigation.
How much of a defense had they put up since they went out of business?
uled that Ross “meant to compete with Westlaw by developing a market substitute.
could have far-reaching implications. Consider how music and image generators compete.
may be useful in revising the Copyright Card Game to addresss AI
As fervent believers in Longterminism, the Silicon Valley elites are not interested in the current multiple crises of our societies. On the contrary, through their social media platforms, Zuckerberg and Musk even instigate further polarization. Climate change, inequality, erosion of democracy – who cares? What counts is the far away future, not the present. Their greatest fear is not the collapse of our climate or the mass extinction of animals – they are haunted by the nightmare of AI taking over control. This would spoil their homo deus party. AI in control doesn’t need humans anymore.
for - biggest worry of silicon valley longterminists - AI takeover, not climate crisis - SOURCE - article - Guido Palazzo
Two technologies are crucial to achieve this wonderful future: rockets to leave this eventually-dying planet and AI merger with the human brain.
for - longterminism - 2 fundamental technologies - rockets and AI
AI systems gather and examine enormous volumes of data, a large portion of which may contain private or sensitive material. There is a chance of abuse or illegal access in the absence of stringent rules and protections. Gaining public trust requires transparency related to data usage with strong security measures in place.
The future of security is AI Surveillance Software Development, offering real-time monitoring, facial recognition, and intelligent threat detection. Advanced AI video surveillance software helps businesses, government agencies, and security firms improve efficiency and reduce risks. By integrating Artificial Intelligence for video surveillance, organizations can automate security monitoring with high accuracy and faster response times.
Im Standard stellt Martin Auber mit aktuellen Daten belegt dar, warum der bloße Ausbau der Kapazitäten zur Erzeugung erneuerbarer Energien nicht zu einer Dekabonisierung führen wird. Der Energiebedarf wächst wesentlich schneller als die zur Verfügung stehende erneuerbare Energiepunkt. Durch den KI-Boom wird er noch einmal deutlich gesteigert. https://www.derstandard.at/story/3000000255154/wann-kommt-die-energiewende-oder-kommt-sie-gar-nicht
MAPPING SOCIAL CHOICE THEORY TO RLHF Jessica Dai and Eve Fleisig ICLR Workshop on Reliable and Responsible Foundation Models 2024
Nice overview of how social choice theory has been connected to RLHF and AI alignment ideas.
Cognition is just one aspect of being human.
for - comment - post - LinkedIn - Bayo - AI
there's all sorts of things we have only the Diest understanding of at present about the nature of people and what it means to be a being and what it means to have a self we don't understand those things very well and they're becoming crucial to understand because we're now creating beings so this is a kind of philosophical perhaps even spiritual crisis as well as a practical one absolutely yes
for - quote - youtube - interview - Geoffrey Hinton - AI - spiritual crisis - AI - Geoffrey Hinton - self - spiritual crisis
quote - AI - spiritual crisis - We only have the dimmest understanding of, at present the nature of people and what it means to have a self - We don't understand those things very well and they're becoming crucial to understand because we're now creating beings - (interviewer: so this is becoming a philosophical, perhaps even spiritual crisis as a practical one) - Absolutely, yes
Poincare anticipated the frustration of an important group of would-be computer users when he said, "The question is not, 'What is the answer?' The question is, 'What is the question?'"
for - Poincare - AI question - SOURCE - paper - Man-Computer Symbiosis - J.C.R. Licklider - 1960 - referred by - Gyuri
the greatest risk is always the bio like biow weapons
for - AI - progress trap - Youtube - bioweapons is not the only threat. Nano technology and many others can be turned into weapons of mass destruction - RDeepSeek R1 just caught up with OpenAIs o1 - There is no moat@ What does this mean? - David Shapiro - 2025, Jan 29
Visual search and AR or augmented reality are emerging technologies that make it possible to revolutionize the shopping style of people. Now, we get to see the innovations with AI. This makes shopping easier and faster. By enabling consumers to view products in authentic environments, augmented reality (AR) goes beyond this. By enabling customers to interact with things before purchasing them, augmented reality (AR) increases engagement. For instance, AR eCommerce platforms let customers virtually test clothes or digitally arrange furniture in their houses.
Explore how artificial intelligence is revolutionizing the retail industry, from optimizing inventory management to creating personalized shopping experiences. Discover the impact of AI in retail industry and its role in driving innovation and efficiency for businesses.
These can be helpful for you, but there are also serious concerns. • Ai can change the authenticity of your writing, turning into a “voice” that is not your own. For example, Grammarly often changes my word choices so they don’t sound like something I’d actually say. That goes beyond just checking grammar. • It can definitely lead to plagiarism, basically creating something that is not from you. • The information is often incorrect or made up, for example citing resources that don’t actually exist.
This resonates with me, so I think after I use grammar correction, I still need to go back and check my writing to express my ideas in a way that suits my style and tone.
for - book - Burnout from Humans: A little book about AI that is not really about AI - Aiden Cinnamon Tea & Dorothy Ladybugboss - 2024
introductory AI courses at Rice University
The Secretary of Defense, in consultation with the Secretary of the Interior, the Secretary of Agriculture, the Secretary of Commerce, and the Secretary of Energy, shall undertake a programmatic environmental review, on a thematic basis, of the environmental effects — and opportunities to mitigate those effects — involved with the construction and operation of AI data centers, as well as of other components of AI infrastructure as the Secretary of Defense deems appropriate. The review shall conclude, with all appropriate documents published, on the date of the close of the solicitations described in subsection 4(e) of this order, or as soon thereafter as possible
March 31st 2025
location within geographic areas that are not at risk of persistently failing to attain National Ambient Air Quality Standards, and where the total cancer risk from air pollution is at or below the national average according to the Environmental Protection Agency’s (EPA’s) 2020 AirToxScreen;
This a reference to sacrifice zones, and not increasing the impact in overburdened. Ommunites already?
possess the characteristics described in subsections (a)(i)-(x) of this section, in a manner that is consistent with the objective of fully permitting and approving work to construct utility-scale power facilities on a timeline that allows for the operation of those facilities by the end of 2027 or as soon as feasible thereafter; and
This counts out nukes
require that, concurrent with operating a frontier AI data center on a Federal site, non-Federal parties constructing, owning, or operating AI infrastructure have procured sufficient new clean power generation resources with capacity value to meet the frontier AI data center’s planned electricity needs, including by providing power that matches the data center’s timing of electricity use on an hourly basis and is deliverable to the data center;
Wow, this is two of the three pillars here pretty explicitly. Additionally is less well defined, but it’s sort of implied elsewhere
The Secretaries shall, to the extent consistent with applicable law and to the extent that the Secretaries assess that the requirement promotes national defense, national security, or the public interest, as appropriate, select at least one proposal developed and submitted jointly by a consortium of two or more small- or medium-sized organizations — as determined by those organizations’ market capitalization, revenues, or similar characteristics — provided that the Secretaries receive at least one such proposal that meets the appropriate qualifications
This seems to rule out the big three / four
Ai in stock trading can allow investors proper access to large quantities of financial information. With this knowledge, they can easily make some better-informed investments. Now, if you ask how to use AI in stock trading, then let’s see the process:
Explore the transformative power of AI in stock trading. From advanced AI stock trading apps and bots to predictive analytics, discover how AI boosts decision-making, optimizes investments, and elevates trading efficiency. Uncover the best AI-powered trading platforms, apps, and software shaping the future of Fintech.
Media literacy impacts who we vote for, how we understand world events, and the decisions we make in our daily lives. Without the ability to critically evaluate information, we’re left vulnerable to manipulation by misinformation, propaganda, and bad actors who exploit our inability to question what we consume. We are currently losing our ability to actively participate in shaping the society we live in.
People shouldn’t be commenting, “Is this real or AI?” on every piece of content they encounter, they shouldn’t be wondering why The Odyssey is a classic, and they certainly shouldn’t be questioning why chapter books “are extremely lengthy.”
They may excel in memorization or standardized test-taking, but when it comes to critical thinking; asking why a text exists, who it is meant for, and how it seeks to influence its audience, there is a noticeable gap. Text within this block will maintain its original spacing when published
I thought it was bad growing up during the “just Google it” age, but as society always manages to outdo itself, the current “just use ChatGPT” mindset is so much worse. At least with Google, there was a semblance of effort: sifting through search results, evaluating sources, and piecing together information to paraphrase for your paper that was due in the next hour. Now, the expectation is instant answers with zero context, no critical thinking, and a growing dependency on AI to do the heavy lifting. It’s not just a shortcut—it’s an exit ramp off the highway of media literacy.
这篇文章由《大西洋月刊》科学台的记者们撰写，列举了2024年让他们震惊的77个事实，涵盖了历史、科技、自然、健康等多个领域，展现了世界的奇妙和多样性。以下是这些事实的详细总结：
以下是文件中提到的52件事的总结：
The most troubling of the findings, to me at least, was the impact of the technology on job satisfaction, especially in terms of their creative contribution, even from the scientists that derived the most value. It’s possible that this is a consequence of the changes and will prove temporary, or it’s possible that this will correct itself by attracting people with different skills and interests to the jobs.
In response, Yampolskiy told Business Insider he thought Musk was "a bit too conservative" in his guesstimate and that we should abandon development of the technology now because it would be near impossible to control AI once it becomes more advanced.
for - suggestion- debate between AI safety researcher Roman Yampolskiy and Musk and founders of AI - difference - business leaders vs pure researchers // - Comment - Business leaders are mainly driven by profit so already have a bias going into a debate with a researcher who is neutral and has no declared business interest
//
for - article - Techradar - Top AI researcher says AI will end humanity and we should stop developing it now — but don't worry, Elon Musk disagrees - 2024, April 7 - AI safety researcher Roman Yampolskiy disagrees with industry leaders and claims 99.999999% chance that AGI will destroy and embed humanity // - comment - another article whose heading is backwards - it was Musk who spoke it first, then AI safety expert Roman Yampolskiy commented on Musk's claim afterwards!
for - article - Windows Central - AI safety researcher warns there's a 99.999999% probability AI will end humanity, but Elon Musk "conservatively" dwindles it down to 20% and says it should be explored more despite inevitable doom - 2024, Ape 2 - AI safety researcher warns there's a 99.999999% probability AI will end humanity
// - Comment - In fact, the heading is misleading. - It should be the other way around. - Elon Musk made the claim first but the AI Safety expert commented on Elon Musk's claim.
Until some company or scientist says ‘Here’s the proof! We can definitely have a safety mechanism that can scale to any level of intelligence,’ I don’t think we should be developing those general superintelligences.We can get most of the benefits we want from narrow AI, systems
for
quote - AI super intelligence is too dangerous, narrow AI can give us most of what we need - Roman Yampolskiy - (see below) - I don’t think it’s possible to indefinitely control superintelligence. - By definition, it’s smarter than you: - It learns faster, - it acts faster, - it will change faster. - You will have malevolent actors modifying it. - We have no precedent of lower capability agents indefinitely staying in charge of more capable agents. - Until some company or scientist says - ‘Here’s the proof! We can definitely have a safety mechanism that can scale to any level of intelligence,’ - I don’t think we should be developing those general superintelligences. - We can get most of the benefits we want from narrow AI, - systems designed for specific tasks: - develop a drug, - drive a car. - They don’t have to be smarter than the smartest of us combined.
// - Comment - Roman Yampolskiy is right. The fact that the industry is pushing ahead full speed with b developing AGI, effectively the same as the AI superintelligence Roman Yampolskiy is referring to - shows the most dangerous pathology of neo capitalism and Technofeudalism, profit over everything else - This feature is a major driver of progress traps
//
Historically, AI was a tool
for - quote - AI: from tool b to agent - Roman Yampolskiy
quote - AI: from tool b to agent - Roman Yampolskiy - (see below)
book, “AI: Unexplainable, Unpredictable, Uncontrollable
for - book - AI: Unexplainable, Unpredictable, Uncontrollable
for - progress trap - AI superintelligence - interview - AI safety researcher and director of the Cyber Security Laboratory at the University of Louisville - Roman Yampolskiy - progress trap - over 99% chance AI superintelligence arriving as early as 2027 will destroy humanity - article UofL - Q&A: UofL AI safety expert says artificial superintelligence could harm humanity - 2024, July 15
when you want to use Google, you go into Google search, and you type in English, and it matches the English with the English. What if we could do this in FreeSpeech instead? I have a suspicion that if we did this, we'd find that algorithms like searching, like retrieval, all of these things, are much simpler and also more effective, because they don't process the data structure of speech. Instead they're processing the data structure of thought
for - indyweb dev - question - alternative to AI Large Language Models? - Is indyweb functionality the same as Freespeech functionality? - from TED Talk - YouTube - A word game to convey any language - Ajit Narayanan - data structure of thought - from TED Talk - YouTube - A word game to convey any language - Ajit Narayanan
ai tipps und tricks, tools-liste
prompting in der lehre - tipps und tricks
Combatting ai-use in school the fun way
An AI customer support chatbot can accurately handle challenging circumstances varies. They catch up on verbal cues, provide fast corrections, and keep the conversation flowing. Voice AI agents are a revolutionary tool for reimagining how businesses engage with their customers because of their unique combination of contextual knowledge and adaptability.
Learn about AI Voice Bot Development and how voice AI bots are revolutionizing customer interactions. Explore the creation of AI bots with voice chat for seamless, natural conversations. Discover the benefits of implementing conversational AI voice bots to enhance user engagement and streamline processes. Perfect for businesses aiming to integrate advanced voice AI technology for customer support or virtual assistants.
I don’t think that using generative AI is conducive to learning as I understand the phenomenon
Agreed for the most part, except for maybe helping a non-native writer with their writing. If it explained to them why it changed their writing, maybe that would be a legitimate learning experience? A colleague has built this to attempt to do that: Revision and Edit Virtual Assistant.
without disclosing it
I recently came across the following, which I really like: The Artificial Intelligence Disclosure (AID) Framework.
IF you decided there were one or two situations where students were allowed to use generative AI, maybe they'd be comfortable using something like this to "admit" and disclose the use of a tool to, for instance, improve the writing of a non-native English writer?