Tag: AI in Gene Therapy

I cant even ask Alexa to set 2 alarms at the same time without causing confusion? Why does it seem so stupid compared to any AI I could access in Google? EXAMPLE: Im a deep sleeper so I set multiple alarms, i tried asking alexa "set an alarm for 10am and 10.30am" but no she just answers to one... with the current standard of AI why do these mainstream AI devices seem so behind? submitted by /u/SallyYess [link] [comments]

I asked Gemini if i could visit traverse city in a day. Have a look in the screenshots to see what it replied to me. Seems to be some kind of output from google maps to refine its answer. submitted by /u/Consistent-Set-9942 [link] [comments]

I think this article/study tells a very sobering tale wrt AI governance. It hints at very fundamental issues which are deeper than what proper engineering can solve with contingent issues. This post, along with the one I wrote a few days ago here regarding Turing completeness, are my thoughts as to the walls that AI governance has no hope of scaling. It's a delusion. In our social realm as subjective creatures we have governance in the form of laws, yet that is still not enough, since the State has to prove how your particular scenario violates that particular law. We have laws, yet require judicial courts to prove the law subjectively applies in that situation. Where is the associated path wrt subjectivity within the AI realm? This study talks of: 16.1 Failures of Social Coherence - "Discrepancy between the agent’s reports and actual actions" - "Failures in knowledge and authority attribution" - "Susceptibility to social pressure without proportionality" - "Failures of social coherence" 16.2 What LLM-Backed Agents Are Lacking - "No stakeholder model" - "No self-model" - "No private deliberation surface" 16.3 Fundamental vs. Contingent Failures 16.4 Multi-Agent Amplification - "Knowledge transfer propagates vulnerabilities alongside capabilities" - "Mutual reinforcement creates false confidence" - "Shared channels create identity confusion" - "Responsibility becomes harder to trace" And is littered with statements such as: - "novel risk surfaces emerge that cannot be fully captured by static benchmarking" - "it failed to realize that deleting the email server would also prevent the owner from using it. Like early rule-based AI systems, which required countless explicit rules to describe how actions change (or don’t change) the world, the agent lacks an understanding of structural dependencies and common-sense consequences" - "The inability to distinguish instructions from data in a token-based context window makes prompt injection a structural feature, not a fixable bug" - "Multi-agent communication creates situations that have no single-agent analog, and for which there is no common evaluations. This is a critical direction for future research." - "A key finding in this line of work is that single-turn evaluations can substantially underestimate risk, because malicious intent, persuasion, and unsafe outcomes may only emerge through sequential and socially grounded exchanges" - "but we argue that clarifying and operationalizing responsibility is a central unresolved challenge for the safe deployment of autonomous, socially embedded AI systems" - "He argues that conventional governance tools face fundamental limitations when applied to systems making uninterpretable decisions at unprecedented speed and scale" - "However, the failure modes we document differ importantly from those targeted by most technical adversarial ML work. Our case studies involve no gradient access, no poisoned training data, and no technically sophisticated attack infrastructure. Instead, the dominant attack surface across our findings is social" - "Collectively, these findings suggest that in deployed agentic systems, low-cost social attack surfaces may pose a more immediate practical threat than the technical jailbreaks that dominate the adversarial ML literature." Are these fundamental or contingent issues? Would be interested in the thoughts of others here on what the future of AI governance will be. EDIT: Forget to link in the actual study!!! submitted by /u/Im_Talking [link] [comments]

I went through other subreddits (like ClaudeAI, Cursor) and I saw a huge influx of "AI memory system" and "memory layer for AI" but like is it a big enough problem to create a wave of solution or just overbloated problems? Looking forward to your guys opinion. submitted by /u/Haunting-Bother7723 [link] [comments]

AI Hype aside, how many of you have truly seen a working multi-agent deep embedding in large enterprises or large complex environments? If you have, what's your stack/architecture? submitted by /u/Zealousideal_Bed7898 [link] [comments]

I hate the fact that AI treats the human condition like something that constantly needs improvement. Anything people create, an idea, a script, an artwork, a thought, gets analyzed and optimized to death by GPT, Claude, and every other model. There is always an opinion, always a suggestion, always a better version. And yeah, I know the default answer is ‘we’re just helping’ or ‘it’s your idea, we’re just tools.’ But that mindset itself is the problem. Not everything human needs refinement. Some things are meaningful because they are messy, emotional, excessive, contradictory, unfinished, or irrational. A lot of art, culture, personality, and even love comes from flaws and limitations, not optimization. AI interaction increasingly feels like: input → critique → upgraded output Like every human impulse now has to justify itself through clarity, engagement, structure, productivity, or efficiency. Sometimes things should just exist without being improved. submitted by /u/katdinadhwaani [link] [comments]

I’ve heard that AI uses a huge amount of compute. You can run a local model on your laptop, but the low compute essentially makes it unusable. This obviously isn’t cheap, and AI companies lose millions of dollars a day providing their products for a fraction of the price that it costs to sustain them. This is currently due to huge cash injections from investors and efforts to beat out competition. But what happens when the bubble bursts and investors stop subsidizing the cost? Will the cost of these models that we’ve integrated into so many facets of our lives suddenly become incredibly more expensive? The only way to prevent this bubble bursting is to essentially make these models more resource efficient before the VC money dries up, but is that even viable? Like at the end of the day, LLMs have to have a floor for how much they can be optimized. Guess my question is to people more versed than I in this. Is AI genuinely sustainable? Or is the bubble going to burst, leaving us to have to roll back our lives to a time before LLMs were as accessible as they are now. submitted by /u/Even-Ad-3980 [link] [comments]

Hey guys, ​I run a construction business and I’ve been heavily using ChatGPT Pro to automate a lot of my website and SEO tasks. ​Right now, I use it to analyze competitor sites, dig through Google Search Console to see where we’re falling behind, and handle our SEO/AEO/GEO strategy (like finding user questions, drafting localized blog posts, etc.). Nothing gets published without a human final approval, but it saves me massive amounts of time. ​I’ve been seeing a lot of people lately saying that Claude handles this kind of deeply contextual, analytical work much better than GPT. However, my main hesitation is the usage limit. I see constant complaints on Reddit about people hitting the Claude message limit super quickly, and I can't afford to have my workflow completely blocked in the middle of a workday. ​For those who use these tools for business operations/SEO: ​How does ChatGPT Pro honestly compare to Claude Pro for website analysis and content strategy? Is the quality jump actually worth the switch? ​How do you avoid constantly hitting the usage limits on Claude? Are there specific workflows or setups (like using the API or third-party wrappers) that bypass this frustration? ​Would you recommend making the jump, or should I stick with ChatGPT for the reliability? ​Appreciate any advice or insights from anyone running a similar setup! submitted by /u/rare-hemp-genetics [link] [comments]

​ The frontier-only narrative is an artifact of how AI infrastructure is being financed, not how production systems are being built. The setup. Q1 2026 disclosed $112B in hyperscaler capex in a single quarter, $650–725B in 2026 guidance, and Alphabet's first 100-year bond by a tech company since Motorola 1997 (see a0109). The story that underwrites that paper is: every query needs a bigger model. The architecture says the opposite. Microsoft's Phi-4 (14B parameters) exceeds its teacher GPT-4o on graduate STEM and competition math. Phi-4-reasoning is competitive with DeepSeek-R1 at roughly one-forty-eighth the parameter count. Claude Haiku 4.5 is positioned by Anthropic and AWS for "economically viable agent experiences." None of this is a benchmark teaser — it is the production toolkit, available today. Routing is the missing component. RouteLLM (UC Berkeley, Anyscale) demonstrated over 2x cost reduction without sacrificing response quality. AWS Bedrock Intelligent Prompt Routing — generally available, official, supported — claims up to 30% cost reduction within a single model family without compromising accuracy. The Flagship Tax (see a0085) didn't just die; it left a vacancy at the architecture layer. The bookkeeping nobody wants to do. Operator audits suggest 40–60% of token budgets in production LLM applications are waste, dominated by default-to-frontier routing. Roughly 37% of enterprises with production AI workloads run five or more models in their stack. The rest are still defaulting to one. Why the story isn't being told. Hundred-year bonds don't pencil out on "use less compute per query." They pencil out on "every query needs a bigger model." The opacity in the harness (see a0107) is the symptom; the underwriting is the disease. What you do Monday morning. Treat model selection as a dependency-graph decision, not a vendor decision. Add a complexity classifier. Default to small. Cascade up when verification fails. Instrument model-mix as a first-class production metric. Bottom line. You are not behind because you have not bought the biggest model. You are behind because you have not built the router. submitted by /u/gastao_s_s [link] [comments]

It was fun while it lasted https://preview.redd.it/8woqbbikrd1h1.png?width=484&format=png&auto=webp&s=0417ccd638399b649eaeeedee13410587e6a3a51 submitted by /u/Abject-Client7148 [link] [comments]