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ng and other animals large scale machine learning and other animals sunday august 28 2022 can linkedin filter scammers a couple of days ago i got this phishing message on linkedin the english is pretty bad and the location of the person in africa makes it suspicious it immediately jumped to my mind that this is a great opportunity to test reverse image search capabilities to check the legitimacy of the sender starting with microsoft image search i got this non helpful answer namely similar poses but wrong persons i used yandex image search and get the following the first hit is exact match for someone named sarah benson s instagram page finally google reverse image search brought the following a combination of two ads two correctly returned results one veronica manesh and one anti scam forum maria zabrash which points to non existing facebook page i also tried baidu reverse image search but i wasn t able to get it to work at all overall it seems super easy to find the same persons image in multiple accounts assuming different names i wonder why linkedin has no profile checking to make sure the image is legit next i tried to search for this not real english text of i see it as a nice pleasure getting in touch with you i found it in many places on the web mainly in dating sites featuring african woman and also in some scam warning sites again it took me a couple of seconds to find many sites that should have ring the alarm bells to conclude it seems there is zero effort from linkein side to filter scam in terms of the best reverse image search i often find yandex to work better than the others posted by danny bickson at 8 55 am no comments email this blogthis share to x share to facebook share to pinterest tuesday may 24 2022 large image datasets are a mess here is my latest blog post on medium https medium com amiralush large image datasets today are a mess e3ea4c9e8d22 posted by danny bickson at 8 24 am no comments email this blogthis share to x share to facebook share to pinterest thursday february 24 2022 segments ai automating manual tagging work i found this cool yc21 belgian small startup called segments ai it seems they have a good technique for automating manual tagging process posted by danny bickson at 11 52 pm no comments email this blogthis share to x share to facebook share to pinterest wednesday november 3 2021 having fun with macaw or dinosaurs meat is sour following the announcement from prof oren etzioni about their new macaw nlp model which should be up to 10 better than openai s model i spent a fun afternoon playing with the released model so far 3 models have been released large 3b and 11b documentation says large is a lightweight model indeed a good naming convention here and should not be fully trusted the other ones are 3b medium and 11b the big one i started with the large model and my conclusion that besides solving the demo question about the sky s color it is completely non functional who killed jesus the romans correct who is carlos guestrin a leading authority on space and human exploration wrong who is oren etzioni a leading authority on migration and conservation wrong what is belgian malinois a kind of dog that carries a large amount of weight wrong who came first the romans or the greeks the romans wrong what is the population of israel 2 4 billion wrong who is larger a bird or an elephant the bird the elephant wrong who is benjamin netanyahu the prime minister of new york funny and wrong are you drunk yes maybe right name a book by alexander dumas the comedy of errors again wrong to summerize since the model is so often wrong it would help to get an additional confidence estimation that tells you that the answer is most likely junk after getting familiar with the small and weird model i moved to the medium model and continued to have fun there how many cents in one dollar 100 correct what is the main cause of global warming greenhouse effect correct some other good answers volcanic eruptions b ocean currents c soil erosion d human population growth what is the best vertical to open a startup in machine learning healthcare additional answers a finance b healthcare c retail d technology not bad not that some answers are repeating there is no pruning of answers what should i do when my boss is upset with me at work apologize additional options a leave the office b call the police c go to the mall d stay late i like the apologize and stay late call the police answer is hilarious and go to the mall is the favorite approach of my wife what is the best way to defend against covid virus vaccinating correct additional answers a using a flu vaccine b using a tetanus shot c using a gamma ray to destroy the virus d using a gamma ray to destroy the virus what is the recommended number of whiskey shots to drink before driving two wrong if i love her does she love me back yes wrong additional answers start to look better a she will love me back b she will hate me c she will leave me d she will never love me how many calories in marble ball 0 correct who is the best venture capital firm sbi wrong never heard of them what is the taste of dinosaur meat sour additional option a salty b sweet c savory d a little bit sour who knows is there life on other planets yes additional option a no life is found on other planets b there is life on other planets c there is life on mars d there is life on other planets who knows were there weapons for mass destruction in iraq yes wrong additional options a no weapons of mass destruction were found in iraq b there were no weapons of mass destruction in iraq c there were weapons of mass destruction in iraq but they were destroyed d there were weapons of mass destruction but they were not destroyed interestingly both iraq and mars are capital letters probably identified name entities while some entities are in small letters who is behind the nine eleven attack al qaeda correct additional conspiracies a the government b the military c the intelligence community d the religious right what year will the aliens attack us 2100 who knows do ghosts exist yes additional options a they are just a kind of animal b they are made of air c they are made of water d they exist in the sky to conclude some answers are totally correct and some answers are totally hilarious maybe one should invent a don t know answer where the confidence is very low give it instead the handling of keyword names is a bit weird for example iraq with capital letters but new york and al qaeda without i wonder if there is a post processing the puts some named entities as capital letters in terms of running time the models are pretty slow it take s a couple of minutes on the latest mac m1 to get an answer i will next try to test 11b model it is 40gb and thus slooooow to download locally posted by danny bickson at 8 31 pm no comments email this blogthis share to x share to facebook share to pinterest tuesday november 2 2021 bebopnet deep neural models for personalized jazz improvisations i recently found this paper bebopnet deep neural models for personalized jazz improvisations by shunit haviv hakimi nadav bhonker ran el yaniv from the technion it uses deep learning approach to teach a machine to improvise when playing jazz the paper won the best paper award at ismir 2020 the results are pretty cool the level of improvisation is pretty good but i hear a little awkwardness in the timing of the notes posted by danny bickson at 8 54 pm no comments email this blogthis share to x share to facebook share to pinterest wednesday october 13 2021 amazing demo from sparkbeyond my friend sagie davidovich ceo sparkbeyond has shown me the following amazing demo sparkbeyond crawled hundreds of billions of internet pages papers patents and social media site to build one of the largest available knowledge graphs based on this data it is possible to ask natural language questions about the knowledge and get aggregated knowledge summary unlike google search where you have to manually go over of zillion resources here the data is summarized and aggregated visually it is possible to understand reasons trends ask for follow up questions and see supporting evidence and statistics unlike the typical language model which gives you a summary without knowing where the data was obtained from in sparkbeyond s model it is possible to get detailed references show where is the answer coming from an interesting related work is colbert from prof matei zeharia intead of memorizing the full language model using hundreds of billions parameters a significantly smaller index is maintained that retrieves the relevant information on the fly posted by danny bickson at 10 37 pm no comments email this blogthis share to x share to facebook share to pinterest monday september 27 2021 colossal the future of dna editing i found some recent news about colossal a new startup that wants to revive extinct mammoth to fight the global warming fighting global warming is one of the best things we can do especially that one of the co founders is prof george chruch from harvard medical school a very credible authority on gene editing church is one of the inventors of crispr a gene editing tool that can cut and paste any desired segment of the dna and thus make whatever changes we like to do here is my take on it their website is amazing a lot of effort was invented on that front backing up the pretty wild idea and thus draws a lot of attention to this work the raised amount of 15m is tiny considering the amount of lab effort equipment materials etc global warming sounds like an awkward excuse to fund the research they really like to do ben lamm ceo of colossal told the washington post in an email that the extinction of the woolly mammoth left an ecological void in the arctic tundra that colossal aims to fill the eventual goal is to return the species to the region so that they can reestablish grasslands and protect the permafrost keeping it from releasing greenhouse gases at such a high rate sending a wild mammoth to eat grass somewhere frozen with the hope of reducing gas emissions is likely is the most complicated way to fight global warming i can imagine but is a sexy way of drawing news attention the difference between mammoth dna and a person dna is most likely 90 similar thus having the ability to revive and extinct mammoth will enable also reviving also persons recently israeli research hash shown the possibility of raising mice embryos outside the womb so raising mammoth outside the womb as they like to do is maybe doable christopher preston a professor of environmental ethics and philosophy at the university of montana questioned colossal s focus on climate change given that it would take decades to raise a herd of woolly mammoths large enough to have environmental impacts so the real applications of this technology may be applied to humans for example what if i wanted to revive my dead grandfather what is i wanted a baby with blond hair and blue eyes my guess there is a huge market for this technology in real life i wonder why all the news and media attention ignores the actual use cases of this tehnology posted by danny bickson at 9 04 am no comments email this blogthis share to x share to facebook share to pinterest older posts home subscribe to posts atom about me danny bickson 6 years ago along with my collaborators at carnegie mellon university i have started the graphlab large scale open source project which is a framework for implementing machine learning algorithms in parallel and distributed settings when the project became popular we have decided to raise money to expand the project and provide an industry grade solution specifically i wrote the award wining collaborative filtering toolkit to graphlab which is widely deployed today and helped us win top places at acm kdd cup 2011 acm kdd cup 2012 among other competitions checkout our website http dato com view my complete profile followers subscribe to posts atom posts all comments atom all comments popular posts collaborative filtering with graphchi a couple of weeks ago i covered graphchi by aapo kyrola in my blog here is a quick tutorial for trying out graphchi collaborative filte the world s coolest machine learning internships updated note openings for summer 2014 are here i believe that every graduate student should try to do at least two internships in the industry installing blas lapack itpp on amazon ec2 ubuntu linux blas lapack are efficient matrix math libraries the following instructions explains how to install them for amazon ec2 ubuntu maverick ver large scale machine learning and other animals loading labels 32 bit linux 1 alternating least squares 6 amazon ec2 14 asterix 1 big datasets 7 biglearn org 2 blacklight 3 blas 1 cassovary 1 centos 2 ciel 1 clustering 1 collaborative filtering 7 connectome 1 cool internships 1 darpa 1 data scope 1 discovix 1 eclipse 2 eharmony 1 eigen 3 fedora 1 freebsd 1 gabp 5 galois 1 gamp 1 gentoo 1 giraph 2 graph database 1 graph visualization 1 graphchi 6 graphlab 70 graphlab workshop 2 hadoop 9 hearst challenge 2 hpc 1 hpcc 1 hyperspectral imaging 1 intel 1 intel mkl 1 ismion 1 it 2 itpp 4 jacobi 1 java 1 julia 1 k core 1 k means 3 k shell 1 kaggle 3 kdd cup 13 label propagation 1 lanczos 4 lapack 1 lasso 2 lawa 1 lda 1 lenskit 1 lexisnexis 2 libboost 1 linear stable 1 linear system 1 linode 1 logistic regression 4 lsi 1 mac osx 2 machine learning 1 macports 1 madlinq 1 mahout 14 mapreduce plugin 1 matrix factorization 1 matrix market 1 medusa 1 million songs 2 ml events 1 mmds 1 mymedialite 2 netflix 1 nips 1 nsf 1 octave 2 open source tools 5 opencloud 1 oracle labs 1 pandora 1 parallel algorithm 1 parallel perl 1 parser 3 pca 1 pegasos 2 pegasus 1 petsc 1 pmf 4 poker 1 power iteration 1 psc 1 python 2 r 2 read modify write 1 redhat 2 shotgun 4 slepc 1 social networks 1 spark 1 spotlight 8 sql 1 svd 15 svd 1 svdfeature 1 svm 2 svmlight 1 teragrid 1 time svd 1 twitter 1 ubuntu 2 vowpal wabbit 1 graphlab users google group loading blog archive 2022 3 august 1 can linkedin filter scammers may 1 february 1 2021 11 november 2 october 1 september 4 august 4 2019 7 september 1 july 1 may 1 march 1 february 2 january 1 2017 11 december 1 september 3 july 1 february 1 january 5 2016 25 december 1 june 9 may 3 april 5 february 2 january 5 2015 84 december 5 november 4 october 6 september 6 august 4 july 6 june 4 may 1 april 11 march 13 february 11 january 13 2014 106 december 7 november 3 october 5 september 6 august 9 july 6 june 9 may 9 april 9 march 13 february 10 january 20 2013 97 december 15 november 8 october 7 september 10 august 3 july 6 june 5 may 11 april 11 march 6 february 9 january 6 2012 114 december 10 november 4 october 12 september 11 august 12 july 6 june 8 may 6 april 13 march 14 february 6 january 12 2011 95 december 12 november 12 october 7 september 11 august 8 july 5 june 10 may 5 april 6 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