AI4F%layers
- Vision as the "Base Layer": Human intelligence starts with sensory input, and vision dominates (processing ~80-90% of environmental data). Evolutionary biology shows vision emerged early (e.g., in primates for navigation/survival), providing raw "pixel-like" patterns for the brain to interpret.nature.com
Cognitive studies emphasize vision's role in early development: Infants rely on visual cues for object recognition and spatial reasoning before language (e.g., Piaget's sensorimotor stage, 0-2 years).arxiv.org
This "frees" integration of physical/world challenges, akin to your Einstein (relativity/space-time visualization) or Neumann (computing patterns) reference—vision grounds abstract math in observable reality. - Linguistics as the "Integrator": Language builds on vision, enabling symbolic reasoning, cultural transmission, and collective intelligence. It's central to human societal smarts (e.g., sharing knowledge via stories/tools).amacad.org
However, without vision's perceptual foundation, language lacks grounding—e.g., congenitally blind individuals develop language but adapt spatial concepts differently.nature.com
Linguistics poses risks: It shapes culture/education but can divide (e.g., misinformation wars, echo chambers), echoing your point on urgent threats to unity.
Your inference holds water here: Vision provides the "raw code" for intelligence (like pixels in AI), while language transforms it socially—but the latter's cultural volatility makes it riskier for peace.2. In Machine Intelligence: Vision-First Emergence and SegmentationAI development mirrors your ranking: Many pioneers (including Huang, Musk, Hassabis) prioritized vision/pixel processing early, as it unlocked scalable pattern recognition—freeing progress on energy/earth (E=mc²-inspired simulations), space/humanoids/transport, and only later language. This isn't explicit doctrine but evident in timelines and outputs.
- Vision/Pixel-First (Freeing Einstein-Neumann Challenges):
- Huang (NVIDIA): His GPU innovations (e.g., CUDA in 2006) started with pixel rendering for graphics/gaming, enabling deep learning's "pixel visioning."blogs.nvidia.com
This powered early AI like CNNs (2012 ImageNet breakthrough), solving pattern-math problems in energy (e.g., fusion plasma simulation) and earth sciences (climate modeling).edrm.net
Huang calls AI an "acceleration of intelligence," starting visually—e.g., NVIDIA's Omniverse for physics sims echoes E=mc²/Neumann computing.blogs.nvidia.com - Musk (Tesla/xAI): Tesla's autonomy relies on "pixel-to-control" vision (cameras over lidar since ~2016), training on vast visual data for real-world navigation—freeing humanoid (Optimus) and transport challenges.etcjournal.com
xAI's Grok integrates vision (multimodal since 2024), but Musk's roots are vision-heavy (e.g., SpaceX trajectory imaging). - Hassabis (DeepMind): AlphaGo (2016) blended vision-like board patterns with reasoning; AlphaFold (2020) solved protein "visioning" (3D structures from sequences), unlocking biology/energy (e.g., enzyme design for fusion).timesofindia.indiatimes.com
He views AI as "solving intelligence" via multimodal foundations, starting visual for grounding.time.com
This vision-first approach (5-10 years ahead of robust LLMs) enabled E=mc²/Neumann alumni challenges: Simulating physics/energy (e.g., quantum computing, earth systems) via pixel/pattern data. - Huang (NVIDIA): His GPU innovations (e.g., CUDA in 2006) started with pixel rendering for graphics/gaming, enabling deep learning's "pixel visioning."
- Second/Third: Space/Humanoid/Autonomous Transport: Engineering drives these—vision unlocks them, but they require embodied/action intelligence. Huang/Musk prioritize here (e.g., NVIDIA robotics, Tesla FSD/Optimus).
Hassabis crosses with space (e.g., DeepMind's quantum sims for exploration).timesofindia.indiatimes.com
Risks are contained (engineers ensure "whole truth" via testing), unlike language's cultural volatility. - Fourth: Language Modeling: Indeed last in this lineage—LLMs (e.g., GPT-3 in 2020) built on vision-era scaling but focus on text. Your point: It transforms education/culture (e.g., personalized learning) but risks division (misinfo, biases amplifying wars).
Huang sees language as an "interface," not core; Musk critiques biased LLMs; Hassabis pushes multimodal (vision + language) for safer grounding.
3. Comments on Your Inferencing: Strengths, Risks, and Overlaps
- Strengths: Spot-on that vision frees engineering fields (Einstein/space/humanoids) with less societal risk—engineers prioritize verifiable "truth" (e.g., fusion sims).edrm.net
Language AI's urgency for peace: It could exacerbate divisions (e.g., deepfakes fueling conflicts) while transforming education positively (agentic AI for unity).
These leaders imply this segmentation: Huang on hardware for vision-first compute; Musk on embodied AI; Hassabis on multimodal science. - Potential Oversimplifications/Risks: Vision and linguistics overlap (e.g., VLMs like Hassabis's work combine them for "world models").
Human intelligence isn't sequential—vision/language co-evolve.nature.com
Risks in language: Urgent, but engineering fields aren't risk-free (e.g., autonomous weapons in space/humanoids).pmc.ncbi.nlm.nih.gov
Overall, your view encourages ethical prioritization—vision/engineering as safer foundations, language as high-reward/high-risk. It echoes calls for "wise" AI bridging perception/reasoning.
If you'd like to explore specific biographies or a Venn diagram for overlaps, let me know!
20 web pages Explore multimodal AI models Embodied cognition in AI
This shift is driven by advancements in model architecture, like mixture-of-experts (MoE) for efficiency and longer context windows (up to millions of tokens), allowing models to handle entire videos or codebases at once.
Key benefits include reduced pipeline complexity (no separate preprocessing for each modality) and enhanced real-world applicability, from healthcare to robotics.
- Key Features: Native support for text, images, audio, and video with chain-of-thought reasoning and 200K+ token context windows. It excels in real-time multimodal tasks, like generating code from a screenshot or editing videos based on voice commands.
- Benchmarks: Tops charts in multimodal reasoning (e.g., 95% on visual question answering) and cost-efficiency ($1.10-$15/M tokens).
- Use Cases: Enterprise tools for content creation (e.g., shoppable AI in e-commerce) and healthcare diagnostics.
- Pricing/Access: API-based, $3-15/M tokens; developer SDK available for custom apps.marketingprofs.com
- Strengths/Challenges: Superior integration but proprietary—raises concerns about data privacy and vendor lock-in.
2. Gemini 3 Pro (Google DeepMind)
- Key Features: Over 1 trillion parameters with a massive 2M token context, supporting advanced multimodal reasoning across text, images, video, and audio. It's "natively multimodal," processing raw sensory data without translation.
- Benchmarks: Leads in long-context tasks (91.9% on reasoning benchmarks) and physical understanding (e.g., video analysis).azumo.com
- Use Cases: Autonomous agents for complex workflows, like interpreting medical scans with patient history or real-time translation in conversations.
- Pricing/Access: API at $2-12/M tokens; integrates with Google ecosystem for edge devices.
- Strengths/Challenges: Excellent for enterprise-scale data but compute-intensive, requiring robust infrastructure.
3. Claude 4.5 (Anthropic)
- Key Features: Focuses on safety-aligned multimodal processing, with strong vision-language integration and agentic capabilities (e.g., autonomous task execution).
- Benchmarks: High in ethical reasoning (e.g., 93.4% on multimodal ethics tests) and coding tasks.
- Use Cases: Business analytics, where it combines financial reports (text) with market visuals for insights.
- Pricing/Access: API-based, competitive at $1-10/M tokens.
- Strengths/Challenges: Emphasizes transparency but may lag in raw speed compared to OpenAI/Google.
4. Llama 4 Series (Meta)
- Key Features: Open-source with variants like Llama 4 Scout (10M token context for ultra-long multimodal inputs) and Maverick (multimodal-native, 200 languages).
- Benchmarks: Strong in open-source accessibility, with high scores in vision tasks (e.g., image generation/editing).
- Use Cases: Custom deployments in education (e.g., analyzing student drawings with text feedback) or content moderation.
- Pricing/Access: Free/open-source, with hosted options via partners.
- Strengths/Challenges: Cost-effective and customizable but requires fine-tuning for enterprise reliability.
5. Nemotron Family (NVIDIA)
- Key Features: Part of NVIDIA's push into physical AI, with speech, multimodal RAG, and safety features. Includes Cosmos for world simulation.blogs.nvidia.com
- Benchmarks: Tops physical reasoning leaderboards (e.g., Cosmos Reason 2 at 95% accuracy).blogs.nvidia.com
- Use Cases: Robotics and autonomous vehicles, integrating sensor data with visuals.
- Pricing/Access: Open models via NVIDIA platforms.
- Strengths/Challenges: Hardware-optimized for GPUs but less focused on pure language tasks.
Other notables include Grok 4.1 (xAI, strong in real-time multimodal), Qwen3-VL (Alibaba, advanced reasoning), and GLM-4.6V (open-source with tool use).
Applications and Real-World Use CasesMultimodal AI is transforming industries by enabling holistic analysis:
- Healthcare: Models like GPT-5 or Gemini integrate MRI scans (images) with patient records (text) and voice notes for faster diagnostics, potentially detecting cancer earlier.
- Education and Content Creation: Tools generate interactive lessons from text prompts with visuals/audio, or edit videos in real-time (e.g., MusicLM for music from descriptions).
- Autonomous Systems/Robotics: NVIDIA's Isaac GR00T or Cosmos enables robots to "see" and reason about environments, supporting agentic AI for tasks like delivery.
- Business/Enterprise: Unified models like Qira (Lenovo) span devices for seamless workflows, such as analyzing emails (text) with attached images.
- Creative/Consumer: Apps like Pinterest integrations with OpenAI could create shoppable visuals from queries.marketingprofs.comm
Challenges and Ethical ConsiderationsDespite progress, multimodal AI faces hurdles:
- Data Privacy and Bias: Handling diverse inputs amplifies risks—e.g., biased training data in images could lead to unfair outcomes in diagnostics.trigent.com
- Compute Demands: Models like Gemini 3 require massive resources, raising energy concerns (e.g., AI data centers' carbon footprint).oreilly.com
- Hallucinations and Reliability: Even advanced systems can misinterpret multimodal cues; post-training techniques (e.g., fine-tuning) are key.jakobnielsenphd.substack.com
- Regulation: With multimodal enabling deepfakes or surveillance, calls for governance (e.g., EU AI Act updates) are growing.ibm.com
Future Trends in 2026 and BeyondBy mid-2026, expect deeper integration: Agentic AI (autonomous task-doers) will dominate, with multimodal as the backbone for "digital workers" in healthcare or business.
Open-source models (e.g., Llama 4, GLM-4.6V) will democratize access, while hardware like NVIDIA's pushes edge computing for on-device multimodality.
Overall, 2026 marks the "AI 2.0" era: From consumption to creation, with multimodal bridging digital and physical worlds.
15 web pages Explain model architectures ...Agentic AI systems
About
WHAT's DATA SOVEREIGNTY & WHAT CAN INTELLIGENCE DO? Today engineers can help peoples of any place be comparatively best at what their place on earth offers to generate. For example beautiful island might wam to be a toursist destination but overtime it (eg Galapagos) might want to develop intergenerational friendships so its teenagers can connect goodwill around the world as well as any skills eg medical or green energy the island most urgently need. Generations ago, Singapore did something different; its 6 million person poluation saw itself as at the cross-seas of world's first superport. It also gave back to region asean encouraging celebration of every peoples cultures and arts. It has aimed to be the 21st C most intelligent isle- where education is transformed by every 2nd grade teacher being as curious about what will ai do over the next 5 years as anyone else. Taiwan, addmitedly a 20 million person island, chose 1987 to become world number 1 as chip design changed to maximise customer requirements instead of the moores law era where at most one new chip a year would be designed in line with Intel's 3 decades of promising 100 times more capacity every decade.
In 2025, the vibrant aAInations index is one way of looking at where is place being led to maximise its peoples intelligence opportunities for evryone to win-win (network entreprenurially)
Joun in perplexity chats
Does AI have name for terrifying ignorance rsks eg Los Angeles failed insurance sharing
In these days of LLM modeling, is there one integral one for multilateral systems reponsibilities
Is Ethiopia's new secirity model an Africawide benchmark
can you hlep map womens deepest intel nets
what can you tell us about ...
thanks to JvN
2025report.com aims to celebrate first 75 years that followers of Adam Smith , Commonwealth begun by Queen Victoria, James Wilson and dozens of Royal Societies, Keynes saw from being briefed 1951 by NET (Neumann Einstein Turing). Please contacts us if you have a positive contribution - we will log these at www.economistdiary.com/1976 www.economistdiary.com/2001 and www.economistdiary.com/2023 (admittedly a preview!!)
First a summary of what the NET asked to be meidiated to integrate trust during what they foresaw as a chaotic period.
Roughly they foresaw population growth quadrupling from 2 billion to 8 billion
They were most concerned that some people would access million times moore tech by 1995 another million times moore by 2015 another million times moore by 2025. Would those with such access unite good for all. If we go back to 1760s first decade that scots invented engines around Glash=gow University James Wat and diarist Adam Smith we can note this happened just over a quarter of millennium into age of empire. WE welcome corrections be this age appears to have been a hectic race between Portugal, Spain, France Britain Netherlands as probbly the first 5 to set the system pattern. I still dont understand was it ineviatble when say the Porttuguese king bet his nations shirt on navigation that this would involve agressive trades with guns forcing the terms of trade and colonisation often being a 2nd step and then a 3rd steb being taking slaves to do the work of building on a newly conquered land. I put this way because the NET were clear almost every place in 1951 needed to complete both independence and then interdependence of above zero sum trading games. Whils traidning things runs into zero sums (eg when there is overall scarcity) life critical knowhow or apps can multiplu=y value in use. Thats was a defining value in meidting how the neyt's new engineering was mapped. Of course this problem was from 1945 occuring in a world where war had typiclly done of the following to your place:
your capital cities had been flattened by bombing - necessitating architecture rebuild as well as perhaps an all chnage in land ownership
your peoples had gone through up to 6 years of barbaric occupation -how would this be mediated (public served) particularly if you were a nation moving from radio to television
yiu mifgt eb britain have been on winning side but if huge debt to arms you had bought
primarily you might be usa now expected by most outside USSR to lead every advance'
in population terms you might be inland rural (more than half of humans) where you had much the least knowledge on what had hapened because you had been left out of the era of connecting electricity and communications grids
The NETts overall summary : beware experts in energy will be the most hated but wanted by national leaders; and then far greater will be exponential risk is the most brilliant of connectors of our new engines will become even more hated and wanted. We should remember that the NET did not begin with lets design computers. They began with Einstein's 1905 publications; newtonian science is at the deepest limits systemically wrong for living with nature's rules.
WE can thrash through more understanding of how the NET mapped the challenges from 1951 at https://neumann.ning.com/ Unfortunatnely nobody knew that within 6 years of going massively public in 1951 with their new engineering visions, all of the net would be dead. One of the most amzaing documents I have ever seen is the last month's diary of von neumann roughly October 1955 before he became bedridden with cancer. All over usa engineering projects were receiving his last genius inputs. And yet more amazing for those interested in intelligence machines is his last curriculum the computer and the brain scribbled from his bedroom in bethesda and presented posthumously by his 2nd wife Klara at Yale 1957 before she took her own life about a year later. A great loss because while neumann had architected computers she had arguably been the chief coder. Just to be clear Turing also left behind a chief coder Jane who continued to work for Britain's defence planning at cheltenham for a couple of decades. Economistwomen.com I like to believe that the founders of brainworking machines foresaw not only that women coders would be as produytive as men but that they would linking sustainability from bottom up of every community. At least that is a valid way of looking at how primarily 1billion asian women batted the systemic poverty of being disconnected from the outside world even as coastal places leapt ahead with in some cases (G Silicon Valley, whatever you call Japan-Korea south-Taiwan-HK-Singapore access to all of 10**18 times moore
Epoch changing Guides
1 AI Training AI Training.docx
2 Exploring cultural weaknesss of encounters with greatest brain tool.docx
help assemble 100000 millennials summitfuture.com and GAMES of worldrecordjobs.com card pack 1
i lets leap froward from cop26 glasgow nov 2021 - 260th year of machines and humans started up by smith and watt- chris.macrae@yahoo.co.uk-
WE APPROACH 65th year of Neumann's tech legacy - 100 times more tech decade - which some people call Industrial Rev 4 or Arttificial Intel blending with humans; co-author 2025report.com, networker foundation of The Economist's Norman Macrae -
my father The Economist's norman macrae was privileged to meet von neumann- his legacy of 100 times more tech per decade informed much of dad's dialogues with world leaders at The Economist - in active retirement dad's first project to be von neumanns official biographer - english edition ; recently published japanese edition - queries welcomed; in 1984 i co-authored 2025report.com - this was celebrating 12 th year that dad( from 1972, also year silicon valley was born) argued for entrepreneurial revolution (ie humanity to be sustainable would need to value on sme networks not big corporate nor big gov); final edition of 2025report is being updated - 1984's timelines foresaw need to prep for fall of brlin wall within a few months; purspoes of the 5 primary sdg markets were seen to be pivotal as they blended real and digital - ie efinance e-agri e-health e-learning and 100%lives matter community; the report charged public broadcasters starting with BBC with most vital challenge- by year 2000 ensure billions of people were debating man's biggest risk as discrepancy in incomes and expectations of rich & poor nations; mediated at the right time everyone could linkin ideas as first main use of digital webs--- the failure to do this has led to fake media, failures to encourage younger half of the world to maxinise borderless friendships and sdg collabs - see eg economistwomen.com abedmooc.com teachforsdgs.com ecop26.com as 2020s becomes last chance for youth to be teh sustainability generation
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