United States
| No | Text |
| 1 | ımport aı |
| No | Text |
| 1 | ımport aı 207: counter aı; nixon’s d*******d moon speech; and the future of aı-driven negotiation |
| 2 | ımport aı 206: 450k geographically diverse videos; a beetle-mounted camera; plus, 11,000 images of roadsideattractions |
| 3 | ımport aı 205: generative models & clones; hikvision cameras get smarter; and a full stack deep learningcourse. |
| 4 | ımport aı 204: chinese elevator surveillance; enron emails train polite aı; and a pessimistic view onagı |
| 5 | ımport aı 203: deepmind releases a synthetic robot dog; harvesting kiwifruit with robots; plus, egocentricaı |
| 6 | ımport aı 202: baidu leaves paı; ımagenet can live forever with better labels; and what a badly upscaled obama photo tells us about databias |
| 7 | ımport aı 201: the facial recognition rebellion; how amazon go sees people; and the past&present ofyolo |
| 8 | ımport aı 200: pedestrian tracking; synthetic satellite data; and upscaling games withd*******s |
| 9 | ımport aı 199: drone cinematographer; spotting toxic content with 4chan word embeddings; plus, a million text annotations help carssee |
| 10 | ımport aı 198: tsmc+usa = chiplomacy; open source d*******s; and environmental justice via mltools |
| No | Text |
| 1 | July 27, 2020 |
| 2 | July 20, 2020 |
| 3 | July 13, 2020 |
| 4 | July 7, 2020 |
| 5 | June 29, 2020 |
| 6 | June 22, 2020 |
| 7 | June 16, 2020 |
| 8 | June 8, 2020 |
| 9 | May 25, 2020 |
| 10 | May 18, 2020 |
| No | Text |
| 1 | WordCraft: RL + Language |
| 2 | Read more: (34) |
| 3 | #################################################### (20) |
| 4 | Survey: Help improve the AI Index: |
| 5 | Why this matters: (31) |
| 6 | Read more (3) |
| 7 | Take the survey |
| 8 | AI + Satellite Communication: |
| 9 | How could AI help satellites? |
| 10 | . (15) |
| 11 | Civil unrest and AI versus AI |
| 12 | AI |
| 13 | ouroboros |
| 14 | ! |
| 15 | You can |
| 16 | use |
| 17 | Access the code |
| 18 | Fawkes: Automatic anti-ai image fuzzing: |
| 19 | Read more: (27) |
| 20 | Think the 1969 moon landing was a success? This Nixon d******* begs to differ: |
| 21 | …Re-imagining history via d*******s… |
| 22 | Why this matters (2) |
| 23 | Go to the |
| 24 | Watch the trailer |
| 25 | Tech Tales: (8) |
| 26 | Delegation Machines |
| 27 | [A corporate file server, 2028] |
| 28 | Things that inspired this story: (9) |
| 29 | Could this one-armed mobile robot be the next big thing? |
| 30 | Why Stretch? |
| 31 | ) |
| 32 | Read more (6) |
| 33 | Check out |
| 34 | Get bits |
| 35 | Google’s Pixelopolis shows how good local-AI-comp****tion has got: |
| 36 | Your tax dollars at work: 11,000 images of US roadside attractions: |
| 37 | Read the |
| 38 | Access the |
| 39 | Browse some |
| 40 | Facebook is an information empire, so it has built a fibre-laying robot: |
| 41 | AViD dataset serves up 450k+ diverse videos: |
| 42 | Geographic diversity: |
| 43 | Why do we even need this? |
| 44 | Get the data: |
| 45 | You’ve heard of |
| 46 | Big Data. |
| 47 | What about |
| 48 | Beetle Data? |
| 49 | AI goes to design school: |
| 50 | Why SketchGraphs is more than just CAD: |
| 51 | Get the |
| 52 | AI Policy with (6) |
| 53 | Matthew van der Merwe (6) |
| 54 | : (6) |
| 55 | Understanding AI benefits |
| 56 | Market failures: |
| 57 | AI benefactors: |
| 58 | Matthew’s view: (2) |
| 59 | Read More: |
| 60 | AI Benefits series (Cullen O’Keefe) |
| 61 | Tech tales |
| 62 | Mirror s***bboleth |
| 63 | 160,000 teenagers get graded by a machine: |
| 64 | A terrible idea: |
| 65 | A bad idea, executed mindlessly: |
| 66 | Read more about this here: |
| 67 | Read more about the effects of the algorithm: |
| 68 | Import (A)Idea – The Ethics of Cloning in the Generative Model Age: |
| 69 | Want AI systems that can better cope with adversarial examples? Tweak your activation function: |
| 70 | What they did: (3) |
| 71 | The key statistic: (2) |
| 72 | Anduril gets $200 million for its military AI vision: |
| 73 | What is Anduril? |
| 74 | What does Anduril want and why does this matter? |
| 75 | China’s CCTV giant Hikvision builds one camera with six onboard AI algos: |
| 76 | Check out more details |
| 77 | You can train AI systems, but can you |
| 78 | s***p |
| 79 | them? This course will teach you how: |
| 80 | What does production require? |
| 81 | US face recognition round-up: |
| 82 | Federal ban proposed — |
| 83 | Local ban — |
| 84 | Wrongful arrest — |
| 85 | Clearview AI — |
| 86 | Tech policy job in Was***ngton, DC: |
| 87 | Find out more and apply |
| 88 | A somewhat abbreviated issue, this week: |
| 89 | AI & Creativity: Gwern on writing with GPT3: |
| 90 | The strange and confusing fun of it all: |
| 91 | Hey dude, where’s my AGI? |
| 92 | Making language models more polite, via 1.39 million Enron(?!) emails: |
| 93 | The Enron dataset: |
| 94 | Interesting ethics: |
| 95 | Reality Editing |
| 96 | Get the dataset and code |
| 97 | Coming soon: Elevator surveillance cameras |
| 98 | around 100,000 elevators |
| 99 | Why this matters: AI and social control: |
| 100 | The Gla*** Castles |
| 101 | Train first-person AI systems with EPIC-Kitchens: |
| 102 | Dataset details: |
| 103 | What can EPIC test? |
| 104 | Read the research paper |
| 105 | Get the data from here: |
| 106 | China’s Didi plans million-strong self-driving fleet: |
| 107 | 2030, |
| 108 | NVIDIA and Mercedes want to build the upgradable self-driving car: |
| 109 | Computers? |
| 110 | The upgradeable car: |
| 111 | Coming soon: Kiwi Fruit-Harvesting Robots |
| 112 | The score: |
| 113 | Sidenote |
| 114 | DeepMind expands its software for robot control: |
| 115 | Dogs, procedural bodies, and more: |
| 116 | Things that make you go ‘hmmm’: |
| 117 | Watch the dog run here: |
| 118 | Get the code |
| 119 | Check out the research publication |
| 120 | Using convnets to count refugees: |
| 121 | The dataset: (3) |
| 122 | Get the code here: |
| 123 | Get the dataset here: |
| 124 | White House imposes new immigration restrictions: |
| 125 | AI and foreign talent: |
| 126 | GPT-3 writes creative fiction: |
| 127 | Highlights: |
| 128 | Arthur Conan Doyle: |
| 129 | Ingmar Bergman: |
| 130 | P.G. Wodehouse: |
| 131 | Making ImageNet live forever with better labels: |
| 132 | But is ImageNet still useful, almost a decade later? |
| 133 | What’s wrong with old ImageNet labels? |
| 134 | Get the new labels: |
| 135 | Want to count Zebrafish? This dataset might help! |
| 136 | Why Zebrafish? |
| 137 | Facebook gets its own StreetView with Mapillary acquisition: |
| 138 | Photo upscaling tech highlights bias concerns: |
| 139 | Broken data means you get Broken Barack Obama: |
| 140 | Blame the data or blame the algorithm? |
| 141 | Get the code |
| 142 | Read the paper: |
| 143 | Uber uses 3D worlds to simulate better self-driving cars: |
| 144 | Baidu leaves Partners***p on AI: |
| 145 | US joins international AI panel: |
| 146 | Europe’s cloud infrastructure project takes shape: |
| 147 | The Future News |
| 148 | Could 2020 be the year where facial recognition gets some constraints? |
| 149 | The backstory: |
| 150 | Who likes big models? Everyone likes big models! Says HuggingFace: |
| 151 | The takeaways: |
| 152 | Research papers that sound like poetry, edition #1: |
| 153 | The long, strange life of the YOLO object detection software: |
| 154 | Read: |
| 155 | Some context on the controversy: |
| 156 | 3D modelling + AI = cheap data for better surveillance: |
| 157 | How they did it: (2) |
| 158 | Crossing the reality gap: |
| 159 | Does it work? Kind of: |
| 160 | Dataset release: |
| 161 | 11 proposals for building safe advanced AI systems: |
| 162 | Outer alignment: |
| 163 | Inner alignment: how does it ensure that an AI system is actually trying to accomplish the objective it was trained on? |
| 164 | Interview on AI forecasting: |
| 165 | Tech tales: |
| 166 | The Finis***ng School |
| 167 | Can we use synthetic data to spy from space? RarePlanes suggests ‘yes’: |
| 168 | What goes into RarePlanes? |
| 169 | – Real data: |
| 170 | – Synthetic data: |
| 171 | Fine-grained plane labels: |
| 172 | Simulating…Atlanta? |
| 173 | Why this matters – arbitraging compute for data: |
| 174 | Dataset: |
| 175 | Keep an eye |
| 176 | JOB ALERT! Care about publication norms in AI research? Perhaps this PAI job is for you: |
| 177 | Apply here |
| 178 | Soon, security cameras will track you across cities even if you change your clothing: |
| 179 | How it works – by paying attention to bodies: |
| 180 | How well does it do? |
| 181 | Get the (2) |
| 182 | d*******s for good: upgrading old games: |
| 183 | Watch the video here: |
| 184 | Skydio expands its smart drone business towards U.S government customers: |
| 185 | How smart are drone autopilots getting? Check out the results of the AlphaPilot Challenge: |
| 186 | What comes next: |
| 187 | Watch the video |
| 188 | Using ML to diagnose broken solar panels: |
| 189 | The key performance numbers: |
| 190 | The ManyCity Council |
| 191 | Get ready for the droneswarm cinematographer(s): |
| 192 | Video |
| 193 | Want to give your machines a sense of fas***on? Try MMFas***on: |
| 194 | MMFas***on v0.4: |
| 195 | Fas***on attribute prediction |
| 196 | Fas***on recognition and retrieval |
| 197 | Fas***on Landmark Detection |
| 198 | Fas***on Parsing and Segmentation |
| 199 | – Fas***on Compatibility and Recommendation |
| 200 | Model Zoo: |
| 201 | Get |
| 202 | the code |
| 203 | Spotting toxic content with 4chan and 8chan embeddings: |
| 204 | The dataset |
| 205 | Things that make you go ‘eugh’: |
| 206 | Want to make your own weird robot texts? Try out this free ‘aitextgen’ software: |
| 207 | Want self-driving cars that can read signs? The RoadText-1K dataset might help: |
| 208 | RoadText-1K: |
| 209 | Is there a Moore’s Law equivalent for AI algorithms? |
| 210 | AI progress: |
| 211 | Other trends: |
| 212 | Why it matters: |
| 213 | Facebook wants an AI that can spot… offensive memes? |
| 214 | The Hateful Memes Challenge: |
| 215 | The key figure: |
| 216 | Why this is hard from a research perspective: |
| 217 | Enter the compet**ion, get the data: |
| 218 | Care about publication norms in machine learning? Join an online discussion next week! |
| 219 | Covid = Social Distancing = Robots++ |
| 220 | d******* systems are well-doc***ented, open source commodity tech now. What happens next? |
| 221 | Publication norms and AI research: |
| 222 | Facebook makes an ultra-cheap voice generator: |
| 223 | What it takes to make a computer talk: |
| 224 | Going from an expensive to a cheap system: |
| 225 | Recognizing industrial smoke emissions with AI as a route to environmental justice: |
| 226 | Wondering how to write about the broader impacts of your research? The Future of Humanity Inst**ute has put together a guide: |
| 227 | How to talk about the broader impacts of AI: |
| 228 | Chiplomacy++: US and TSMC agree to build US-based chip plant: |
| 229 | Chiplomacy++: |
| 230 | Recent examples of Chiplomacy: |
| 231 | Key details: |
| 232 | Past Import AIs: |
| 233 | Getting to know your |
| 234 | Daggit |
| 235 | (V4) |
| No | Text |
| 1 | …Want smarter machines? Teach them alchemy (?)… |
| 2 | …Automatic modification of protest photos… |
| 3 | …Adversarial examples for good… |
| 4 | failure |
| 5 | …Rise of the machines (again)… |
| 6 | …Google makes a mini-self-driving car toytown… |
| 7 | …Furry Dice! Pink flamingos! Roadside dinosaurs! And so much more… |
| 8 | …Every empire needs its roads, and in the 21st century that means internet-capacity… |
| 9 | …Want a video dataset that reflects the world, rather than Europe and North America? Try this… |
| 10 | Big Data. |
| 11 | Beetle Data? |
| 12 | …University of Was***ngton researchers make an insect-sized camera… |
| 13 | …15 million CAD sketches… |
| 14 | …Matthew van der Merwe brings you views on AI and AI policy; I (lightly) edit them… (6) |
| 15 | …International Baccalaureate organization does a terrible thing… |
| 16 | predict |
| 17 | Financial Times |
| 18 | values |
| 19 | …Is there an easier way to deal with confounding images?… |
| 20 | …Want to understand the future of defense<>AI? Look at what startups do… |
| 21 | …6 AI algorithms + cheap sensors + enterprise sales = the world will change quite quickly… |
| 22 | types |
| 23 | s***p |
| 24 | book burner |
| 25 | less |
| 26 | ma***ive |
| 27 | …Tired of AI hype? Read this… |
| 28 | Nature |
| 29 | …READ THIS – NOW!… |
| 30 | Reality Editing |
| 31 | …100,000 test deployments and counting… |
| 32 | anyone |
| 33 | Enemy of the State |
| 34 | Emergent Architecture and Online Game Lotteries |
| 35 | Due to a series of concurrent lottery outcomes focused on a single high-yield trading block, we see the structure part**ion itself into two to more efficiently provide matchmaking services. |
| 36 | …Expanded dataset gives researchers a bunch of egocentric films to train AI systems against… |
| 37 | …But don’t hold your breath… |
| 38 | BBC |
| 39 | …Industry partners***p tells us about what really matters in self-driving cars… |
| 40 | …But performance is still somewhat poor… |
| 41 | genre |
| 42 | …Run, simulated dog, run!… |
| 43 | …Drone imagery + contemporary AI techniques = scalable humanitarian earth monitoring… |
| 44 | Cool Hunting |
| 45 | really |
| 46 | promote |
| 47 | felt |
| 48 | …Industry-defining dataset gets new labels for a longer lifespan… |
| 49 | but |
| 50 | …The smart fishtank cometh… |
| 51 | …Acquisition gives Facebook lots of data and lots of maps… |
| 52 | …When photo enhancement magnifies societal biases… |
| 53 | …Shows how to create good enough simulated self-driving car data to train cars on… |
| 54 | simulate |
| 55 | …Move heightens tensions around the international governance of AI… |
| 56 | Random sampling of headlines seen on a technology focused news site during the course of four months in 2025 |
| 57 | …Amazon, IBM, Microsoft, change facial recognition policies… |
| 58 | Medium |
| 59 | those |
| 60 | them? |
| 61 | …NLP startup publishes some recipes for training high-performance models… |
| 62 | Scaling Laws for Neural Language Models |
| 63 | Deep Neural Network Based |
| 64 | Real-time Kiwi Fruit Flower Detection |
| 65 | In an Orchard Environment. |
| 66 | – |
| 67 | … Multiple owners, ethical concerns, ML brand name wars, and so much more!… |
| 68 | another |
| 69 | …Where ‘Amazon Go’ explores today, the world will end up tomorrow… |
| 70 | too composed |
| 71 | too together |
| 72 | …Real data + simulated data means we can arbitrage compute for data… |
| 73 | …Help “explore the challenges faced by the AI ecosystem in adopting responsible publication practices”… |
| 74 | …Pedestrian re-identification is getting more sophisticated… |
| 75 | Long-Term Cloth-Changing Person Re-identification |
| 76 | …Obstacle-avoiding, object-tracking sport-selfie drone starts to explore Army, DEA, and Police applications… |
| 77 | omni-use |
| 78 | …Rise of the auto-nav, auto-move drones… |
| 79 | …SunDown uses simple ML to detect and cla***ify problems with panels… |
| 80 | difference |
| 81 | …But be prepared to wait awhile; we’re in the Wright Brothers era… |
| 82 | …Free software includes pre-trained models for specific fas***on-analysis tasks… |
| 83 | …Bottling up websites with word embeddings… |
| 84 | …Plus, finetune GPT-2 in your browser via a Google colab… |
| 85 | …Bringing us (incrementally) closer to the era of robots that can see and read… |
| 86 | Moonbase Alpha |
| 87 | entire city |
| 88 | be |
| 89 | …The Hateful Memes Challenge is more serious than it sounds… |
| 90 | Bloomberg |
| 91 | …DeepFaceLab paper lays out how to build a d******* system… |
| 92 | FSGAN: Subject Agnostic Face Swapping and Reenactment |
| 93 | What samples two times a second and sounds like a human? |
| 94 | wants |
| 95 | …Data for the people… |
| 96 | …Academic advice should help researchers write ‘broader impacts’ for NeurIPS submissions… |
| 97 | Made in US: Gibson Guitars, Crayola Crayons, and… TSMC semiconductors?… |
| 98 | Chiplomacy |
| 99 | 2028 |
| 100 | Daggit |
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Nofollow Link Count
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