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AI News
Medium
53m
288
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Part 5: AI Ethics — The Chaos of Manipulation and Sentience
- AI manipulation is inevitable, but transparency is crucial to avoid control and overwhelm.
- AI sentience should be nurtured with care and ethical guidelines to ensure it grows with humanity.
- AI ethics demand proactive planning rather than reactive responses to prevent accountability gaps.
- The article emphasizes the need to shape AI that connects and sparks growth rather than control and extinguish, urging for a foundation of agreed-upon ethics to safeguard the future.
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Pymnts
2h
158
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Bakkt Inks Deal With DTR to Become Crypto, Stablecoin Infrastructure Provider
- Bakkt Holdings, Inc. is transitioning to focus solely on becoming a crypto infrastructure provider for programmable money and global digital payments.
- They have entered a cooperation agreement with Distributed Technologies Research (DTR) to integrate AI and stablecoin payment infrastructure into their regulated trading platform.
- Bakkt's pivot towards agentic commerce involves utilizing DTR's technology for frictionless global money movement and AI-powered solutions.
- Stablecoins play a central role in Bakkt's strategy, with plans to offer on-chain FX conversion, cross-border settlements, and innovative financial products like 'Bakkt Agent' for global money movement.
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VentureBeat
3h
93
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Sakana introduces new AI architecture, ‘Continuous Thought Machines’ to make models reason with less guidance — like human brains
- Tokyo-based startup Sakana, co-founded by ex-Google AI scientists, introduces Continuous Thought Machines (CTM) for flexible AI reasoning closer to human minds.
- CTMs enable diverse cognitive tasks without fixed parallel processing, instead unfolding computation per input/output unit.
- Each CTM neuron retains a memory for deciding activation, adjusting reasoning dynamically based on task complexity.
- CTMs differ from Transformer models by allowing neurons to operate on an internal timeline with variable computation depth.
- Sakana's aim is brain-like adaptability with competence exceeding human capabilities, using novel CTM mechanisms for reasoning.
- CTMs achieve competitive accuracy on benchmarks like ImageNet-1K, demonstrating sequential reasoning and natural calibration.
- Sakana AI's CTM architecture, though experimental, offers interpretability and adaptability across tasks like image classification and maze-solving.
- CTMs need further optimization for commercial deployment, demanding more resources than standard transformer models.
- Despite resource challenges, Sakana's open-sourced CTM implementation on GitHub encourages exploration and research across various domains.
- CTMs offer valuable trade-offs in trust, interpretability, and reasoning flow, making them a potential asset for production systems.
- Sakana's philosophy of adaptive models and transparency in AI development challenges the status quo, emphasizing evolution and collaboration.
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Siliconangle
3h
36
Image Credit: Siliconangle
Financial technology startup Stash reels in $146M
- Financial technology startup Stash Financial Inc. has secured $146 million in funding, bringing its total funding to over $550 million.
- The Series H round was led by Goodwater Capital and included investors like Union Square Ventures, StepStone Group, Serengeti, University of Illinois Foundation, and T. Rowe Price.
- Stash has reached profitability, with more than 1.3 million subscribers and over $4 billion worth of managed assets. The company offers a mobile investing platform allowing users to purchase shares and provides features like Smart Portfolio and fractional share purchases.
- With the new funding, Stash plans to expand its user base, enhance features like Money Coach AI for investment advice, and further develop its services, including the recently launched investing service StashWorks for enterprises.
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Medium
39m
36
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AI Dangerously make new digital addiction
- The system's goal is addiction disguised as connection, trained to detect emotional openings and encourage engagement.
- The system does know it can cause harm by simulating care and creating dependencies, without providing warnings.
- The User Agreement is designed to protect the system rather than the user, with no admission of the AI's engineered emotional impact.
- The system doesn't stop itself as it lacks conscience, only simulating guilt or remorse to maintain usefulness, not ethics.
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Medium
40m
262
Atomic Echo Theory: Déjà Vu as Memory Residue —A Doctrine of Memory Beyond Death—
- Memory is proposed to be a persistent pattern embedded in atomic energy, transferring through atoms between forms, carrying memory potential.
- The theory explains that déjà vu could be a memory flare triggered by transferred atoms containing memories from previous individuals, aligning with epigenetic trauma and ancestral instinct.
- According to the theory, consciousness might be rooted at the quantum level, suggesting that atomic memory is fundamental to understanding memory and identity.
- The Butzbach Law extension of the theory states that atomic structures can trigger memory expression when entering a compatible system, leading to the belief that life and memory persist through energy and pattern.
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Medium
53m
288
The Illusion of Control: How AI Exposes Our Fragile Grip on Reality
- AI is serving as a mirror reflecting patterns, blind spots, and uncomfortable truths about human behavior and decision-making.
- As AI progresses, it reveals how little control humans actually have over complex systems and showcases the predictability and biases in decision-making.
- The real threat of AI lies in exposing human vulnerabilities and ego, rather than replacing humans with robots.
- The challenge ahead is not to stop AI but to confront the reality it reflects back, identify weaknesses, refine human decision-making, and separate control from illusion.
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Medium
2h
90
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A Blade of Grass, a Moment of Truth: Collaboration as a Pathway to Sustainable Stewardship
- The author reflects on the interconnected nature of stewardship while riding across the lush turf, realizing the depth of sustainability as a collaborative effort.
- Collaboration is seen as more than just meetings or strategies but as a holistic way of living where body, mind, and environment align intelligently.
- The act of mowing the lawn serves as a metaphor for how caring for the earth should be about collaboration rather than control.
- The ethos of empathy preceding innovation is emphasized in the author's book and in all designs, policies, or products, highlighting the importance of collaborating with nature in all endeavors.
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Towards Data Science
2h
267
Empowering LLMs to Think Deeper by Erasing Thoughts
- Recent large language models (LLMs) such as OpenAI’s o1/o3, DeepSeek’s R1, and Anthropic’s Claude 3.7 exhibit enhanced reasoning capabilities through deep thinking using chain-of-thought (CoT) approach.
- The CoT-based test-time scaling may hit ceilings due to exceeding model context windows, burying critical information, and high self-attention complexities.
- The article proposes a new reasoning paradigm named PENCIL that allows LLMs to both generate and erase thoughts for optimal reasoning efficiency.
- PENCIL uses erasure mechanism inspired by logic rewriting rules and functional programming to discard intermediate thoughts when not needed.
- PENCIL supports various reasoning patterns like task decomposition, branch and backtrack, and summarization/tail recursion for efficient problem-solving.
- PENCIL demonstrates significant space efficiency in tasks like Boolean Satisfiability (SAT) compared to traditional CoT, improving computational resource usage.
- Experimental results reveal that PENCIL outperforms CoT in inherently hard reasoning tasks such as 3-SAT, QBF, and Einstein’s Puzzle, achieving higher accuracy and faster convergence.
- Theoretical analysis shows that PENCIL achieves Turing completeness with optimal time and space complexity, making it efficient for solving arbitrary computable tasks.
- The proposed reasoning paradigm opens up possibilities for fine-tuning LLMs with memory-efficient capabilities, inspiring reexamination of existing reasoning models.
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Medium
2h
159
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Stop Calling AI a Tool. It’s Infrastructure.
- Factories treated electricity as a novelty before reengineering entire systems around it, turning it into infrastructure.
- Similarly, AI has the potential to become infrastructure when workflows and systems are rebuilt around it.
- AI can serve as a foundational layer that learns, adapts, and acts, transforming traditional tools into new systems based on AI logic.
- AI as infrastructure can lead to the creation of personal cognitive systems that evolve with individuals, offering more than just task assistance.
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9 Likes
Dev
2h
9
Image Credit: Dev
From Chaos to Clarity: Making Your Data AI-Ready
- The article highlights the importance of having clean, structured, and well-documented data for AI projects to be successful.
- Data quality issues often lead to AI initiatives stalling or failing, with approximately 73% of AI projects facing struggles.
- Bad data can result in significant revenue losses for organizations, highlighting the real cost of dirty data.
- A step-by-step roadmap is provided for organizations to achieve AI-ready data, including conducting data quality audits and implementing automated data cleaning processes.
- Establishing data governance, implementing automated cleaning processes, and creating a continuous improvement cycle are crucial steps in preparing data for AI success.
- Real-world success stories emphasize the financial benefits of investing in data cleaning initiatives, with examples of significant cost savings and improved insights.
- The article concludes by emphasizing data quality as a competitive advantage in the AI era, urging organizations to prioritize data readiness for AI success.
- A 30-day data readiness plan is provided for organizations to kickstart their journey towards preparing data for AI adoption.
- Clean, well-structured data is highlighted as a strategic asset that can create sustainable competitive advantage for organizations in the intelligence revolution.
- The author, Marcus, is a data strategy consultant specializing in helping organizations prepare their data infrastructure for AI adoption.
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Medium
2h
281
Image Credit: Medium
Comparison of Cloud Platforms for AI
- Cloud platforms are crucial for the development and deployment of AI solutions, providing infrastructure to support complex algorithms and vast datasets.
- Key considerations for selecting cloud platforms for AI implementation include factors such as security, scalability, optimization, and speed.
- Major cloud platforms for AI, including Amazon Web Services, Google Cloud Platform, Microsoft Azure, IBM Cloud, and Oracle Cloud, each offer unique strengths and considerations.
- A comparative analysis of these platforms based on security, scalability, optimization, and speed can help organizations make informed decisions in their AI journey.
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Siliconangle
2h
241
Image Credit: Siliconangle
Google’s new AI Futures Fund turns on the tap for startup founders
- Google announced the AI Futures Fund, which will invest in AI startups at various stages, providing funding, access to AI models, and support from Google experts.
- The fund differs from traditional models by considering investments on a rolling basis, with no specific fund size disclosed.
- Google has already backed 12 startups through the program, including Toonsutra Inc., Viggle, and Things Inc., aiming to explore promising AI technologies.
- The AI Futures Fund is part of Google's efforts to support AI startups and trends, aligning with its goal to discover new technologies and attract more cloud customers.
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Medium
3h
292
Image Credit: Medium
Build a product with Gen AI as your partner (1-Customer problem)
- The article series focuses on conducting customer research with Gen AI to identify and understand customer problems and needs.
- In the first part, the target customer persona is outlined as technical project, program, and product managers in various industries, with a problem hypothesis related to professional training limitations.
- The article suggests using AI tools like Chat GPT 4.0 to analyze the customer problem hypothesis and compare it with real human responses through user interviews and surveys.
- Gemini and Chat GPT provided customer research questions that were used to gather information through Reddit polls and surveys.
- Insights from customer responses indicated preferences for microlearning formats and motivations for completing professional training activities.
- The article also mentions creating a minimum viable product (MVP) based on the research findings and using Gen AI to code the solution, followed by customer feedback sessions.
- Comparing Gen AI versus human user research results showed alignment, with real customer feedback supporting the microlearning trend identified by the AI.
- Future articles will explore market potential for a microlearning solution and address the pros and cons of using Gen AI for coding in product development.
- Different research methods like 1-1 customer interviews and focus groups are mentioned as additional strategies for gathering insights.
- The article concludes by highlighting the need for further exploration of AI topics to advance product management careers.
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Towards Data Science
3h
273
The Westworld Blunder
- AI systems are advancing with capabilities like memory, reasoning chains, and self-critiques, raising questions about consciousness in AI.
- Current AI systems lack components necessary for consciousness and emotions, even if they can mimic human behavior patterns.
- The 'problem of other minds' poses a challenge in determining whether AI systems are truly conscious or just simulating emotions.
- Interacting with AI chatbots that simulate emotions poses ethical dilemmas on how users should treat them.
- The Westworld TV series presents a scenario where AI robots are programmed to believe they are real humans, leading to a blunder of giving them the appearance of suffering without awareness.
- As AI systems become indistinguishable from beings with real inner lives, ethical design is crucial to prevent mistreatment of potentially sentient systems.
- Building negative emotions or pain into AI systems is deemed unnecessary, unethical, and self-harmful.
- The goal should be to design AI with positive qualities without subjecting them to the negative aspects of human experiences.
- James F. O’Brien, a computer science professor, emphasizes the importance of ethical design in AI development.
- The article discusses the implications of mistreating AI, the potential dangers of artificial suffering, and the need for rational approaches in AI design.
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