References for Brightwork AI & ML Articles
Executive Summary
- These are the references that were used for our AI & ML articles.
Learn why so few entities in the IT space include references in their work.
Introduction
This is the reference list for the AI & ML articles, as well as interesting quotes from these references at Brightwork Research & Analysis.
You can select the article title to be taken to the article.
Reference #1: Article Titled:
ComputerWeekly’s Strange Poll Results on Data Science
https://www.computerweekly.com/news/252474752/Most-data-scientists-plan-exit-in-2020-Women-in-Data-conference-hears
Reference #2: Article Titled:
The Constant Privacy Invasion That Comes Along With AI and SaaS
https://www.grammarly.com/privacy-policy#grammarlys-business-model
Reference #3: Article Titled:
How Accurate Was Marvin Minsky in His AI Predictions?
*https://www.theverge.com/2019/8/9/20798900/marvin-minsky-jeffrey-epstein-sex-trafficking-island-court-records-unsealed
*https://web.media.mit.edu/~minsky/
*https://en.wikipedia.org/wiki/Marvin_Minsky
*https://en.wikipedia.org/wiki/Artificial_neural_network
*https://www.scribd.com/article/401160340/Stop-Saying-exponential-Sincerely-A-Math-Nerd
*https://www.theguardian.com/technology/2016/feb/03/marvin-minsky-obituary
*https://en.wikipedia.org/wiki/Frank_Rosenblatt
*https://www.vanityfair.com/news/2017/03/elon-musk-billion-dollar-crusade-to-stop-ai-space-x
*https://medium.com/@windycityn0velist/musk-misses-the-stories-you-dont-hear-about-tesla-anymore-8dfce1d803d2
*https://www.ccn.com/elon-musks-backtrack-tesla-robotaxi-claim-securities-fraud/
*https://www.latimes.com/business/autos/la-fi-hy-tesla-musk-annual-meeting-shareholders-20190611-story.html
*https://medium.com/@hypergiant/is-neural-network-hype-killing-machine-learning-120041406f1
*https://www.kdnuggets.com/2017/08/37-reasons-neural-network-not-working.html
*https://www.analyticsinsight.net/neural-networks-not-answer-everything/
*http://neuralnetworksanddeeplearning.com/chap5.html
*https://builtin.com/data-science/disadvantages-neural-networks
*https://towardsdatascience.com/ants-and-the-problems-with-neural-networks-778caa73f77b
*https://towardsdatascience.com/neural-networks-problems-solutions-fa86e2da3b22
*https://en.wikipedia.org/wiki/Backpropagationhttps://www.youtube.com/watch?v=t81HiFOqbAs
*https://www.technologyreview.com/2016/01/26/163622/what-marvin-minsky-still-means-for-ai/
*https://en.wikipedia.org/wiki/Computing_Machinery_and_Intelligencehttps://www.the-scientist.com/magazine-issue/artificial-intelligence-versus-neural-networks-65802
*https://www.youtube.com/watch?v=aircAruvnK
*khttps://www.youtube.com/watch?v=S3Y-TeLKMP8
*https://www.youtube.com/watch?v=ILsA4nyG7I0
*https://www.bbc.co.uk/teach/ai-15-key-moments-in-the-story-of-artificial-intelligence/zh77cqt
*https://www.forbes.com/sites/cognitiveworld/2019/10/20/are-we-heading-for-another-ai-winter-soon/#3fb8536256d6
*https://en.wikipedia.org/wiki/Lighthill_report
*https://www.newscientist.com/article/dn16306-us-investigation-into-gravity-weapons-nonsense/
*https://towardsdatascience.com/probability-of-an-approaching-ai-winter-c2d818fb338a
*http://www.chilton-computing.org.uk/inf/literature/reports/lighthill_report/p004.htm
*https://www.theguardian.com/technology/2018/jul/25/ai-artificial-intelligence-social-media-bots-wronghttps://www.kurzweilai.net/
*https://www.siliconrepublic.com/machines/marvin-minsky-ai-predictions
*https://en.wikipedia.org/wiki/Perceptrons_(book)https://www.newyorker.com/news/news-desk/is-deep-learning-a-revolution-in-artificial-intelligence
*https://www.edge.org/conversation/marvin_minsky-remembering-minsky
*https://paw.princeton.edu/article/lives-marvin-minsky-54
*https://www.quora.com/Whats-Marvin-Minskys-view-on-deep-learning
*https://www.quora.com/What-was-Marvin-Minsky-wrong-about
*https://www.newyorker.com/magazine/1981/12/14/a-i
*https://www.quora.com/Why-does-Marvin-Minsky-hate-Noam-Chomskys-linguistic-theories
Reference #4: Article Titled:
Why Did AI Rise for a Third Time After the 1st and 2nd AI Winters?
https://www.theguardian.com/technology/2016/feb/03/marvin-minsky-obituary
https://towardsdatascience.com/probability-of-an-approaching-ai-winter-c2d818fb338a
Reference #5: Article Titled:
First AI Winter and What The Lighthill Report Said About Progress
https://en.wikipedia.org/wiki/Lighthill_report
http://www.chilton-computing.org.uk/inf/literature/reports/lighthill_report/p004.htm
https://www.newscientist.com/article/dn16306-us-investigation-into-gravity-weapons-nonsense/
Reference #6: Article Titled:
The AI, Big Data and Data Science Bubble and the Madness of Crowds
https://www.amazon.com/AI-Delusion-Gary-Smith/
https://www.forbes.com/sites/cognitiveworld/2019/10/20/are-we-heading-for-another-ai-winter-soon/#3fb8536256d6
https://www.forbes.com/sites/gilpress/2014/12/03/the-end-of-the-hadoop-bubble/#668a859a3ca2
Reference #7: Article Titled:
The Real Plan Companies Have for AI in Workplaces
https://mattstoller.substack.com/p/how-cvs-became-a-health-care-tyrant
Reference #8: Article Titled:
The Enormous Exaggeration and Lack of Fact Checking of Big Data Claims
*https://www.amazon.com/AI-Delusion-Gary-Smith/
https://towardsdatascience.com/probability-of-an-approaching-ai-winter-c2d818fb338a
https://www.forbes.com/sites/joemckendrick/2020/01/30/forget-the-roi-with-artificial-intelligence-decision-making-will-never-be-the-same/#6f9b3b213f7f
https://towardsdatascience.com/the-future-of-data-is-fake-694d2aa0d3d5
Reference #9: Article Titled:
How AI Projects Human Features onto Inanimate Objects
*https://www.amazon.com/AI-Delusion-Gary-Smith/
*https://www.amazon.com/You-Look-Like-Thing-Love-ebook/dp/B07PBVN3YJ/
Reference #10: Article Titled:
The Problem with the Term Data Science
https://blogs.wsj.com/cio/2014/05/02/why-do-we-need-data-science-when-weve-had-statistics-for-centuries/
https://www.theguardian.com/technology/2018/jul/25/ai-artificial-intelligence-social-media-bots-wrong
https://www.amazon.com/AI-Delusion-Gary-Smith/
https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century
*http://radar.oreilly.com/2011/05/data-science-terminology.html
https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/#ee628ab55cfc
https://en.wikipedia.org/wiki/Science
https://en.wikipedia.org/wiki/Data_science
https://towardsdatascience.com/is-data-science-really-a-science-9c2249ee2ce4
Reference #11: Article Titled:
The Complicity of the Media in Pushing The AI Bubble
https://www.theguardian.com/technology/2018/jul/25/ai-artificial-intelligence-social-media-bots-wrong
https://www.amazon.com/AI-Delusion-Gary-Smith/
Reference #12: Article Titled:
The Statistical Falseness of Much AI, Data Science, and Big Data Results
https://www.theverge.com/2019/3/5/18251326/ai-startups-europe-fake-40-percent-mmc-report
*https://www.amazon.com/AI-Delusion-Gary-Smith/dp/0198824300/
https://www.oreilly.com/data/free/being-a-data-skeptic.csp
Reference #13: Article Titled:
What Happened to the Second AI Bubble’s Expert Systems?
https://en.wikipedia.org/wiki/Expert_system
https://www.jstor.org/stable/2582480?seq=1
https://www.quora.com/Why-do-AI-researchers-not-develop-any-more-expert-systems
Reference #14: Article Titled:
How Accurate Was Dr. Hubert Dreyfus on AI Predictions in His Book What Computers Can’t Do?
https://en.wikipedia.org/wiki/Artificial_neural_network
https://simple.wikipedia.org/wiki/Ontology
https://en.wikipedia.org/wiki/Expert_system
https://en.wikipedia.org/wiki/Learning
https://towardsdatascience.com/history-of-the-first-ai-winter-6f8c2186f80b
*https://www.amazon.com/What-Computers-Still-Cant-Artificial-ebook/dp/B002XQ21X8
https://towardsdatascience.com/history-of-the-first-ai-winter-6f8c2186f80b
Reference #15: Article Titled:
The Problematic Black Box Nature of Neural Networks and Deep Learning
https://en.wikipedia.org/wiki/Artificial_neural_network
*https://www.amazon.com/Rebooting-AI-Building-Artificial-Intelligence-ebook/dp/B07MYLGQLB
https://www.technologyreview.com/s/612072/artificial-intelligence-is-often-overhypedand-heres-why-thats-dangerous/
Reference #16: Article Titled:
The Overestimation of Neural Networks and Deep Learning
https://en.wikipedia.org/wiki/Artificial_neural_network
https://www.scribd.com/article/401160340/Stop-Saying-exponential-Sincerely-A-Math-Nerd
*https://www.amazon.com/Rebooting-AI-Building-Artificial-Intelligence-ebook/dp/B07MYLGQLB
https://www.technologyreview.com/s/612072/artificial-intelligence-is-often-overhypedand-heres-why-thats-dangerous/
Reference #17: Article Titled:
Who Was More Accurate, Marvin Minsky or Hubert Dreyfus on AI?
https://en.wikipedia.org/wiki/Artificial_neural_network
https://en.wikipedia.org/wiki/Seymour_Papert
https://towardsdatascience.com/history-of-the-first-ai-winter-6f8c2186f80b
*https://www.amazon.com/Rebooting-AI-Building-Artificial-Intelligence-ebook/dp/B07MYLGQLB
*https://www.amazon.com/What-Computers-Still-Cant-Artificial-ebook/dp/B002XQ21X8
Reference #18: Article Titled:
Why is Brightwork Research Interested in Evaluating AI Claims?
https://en.wikipedia.org/wiki/Artificial_neural_network
https://www.forbes.com/sites/joemckendrick/2020/01/30/forget-the-roi-with-artificial-intelligence-decision-making-will-never-be-the-same/#6f9b3b213f7f
*https://www.amazon.com/Rebooting-AI-Building-Artificial-Intelligence-ebook/dp/B07MYLGQLB
Reference #19: Article Titled:
Should Machine Learning be Instead Called Machine Identification?
https://en.wikipedia.org/wiki/Artificial_neural_network
Reference #20: Article Titled:
Why Train Neural Networks When One Can Perform Preset Programming?
https://en.wikipedia.org/wiki/Artificial_neural_network
http://news.mit.edu/2020/artificial-intelligence-identifies-new-antibiotic-0220
http://news.mit.edu/2017/explained-neural-networks-deep-learning-0414
*https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi&index=1
An interesting note about the processors used for neural networks.
The complex imagery and rapid pace of today’s video games require hardware that can keep up, and the result has been the graphics processing unit (GPU), which packs thousands of relatively simple processing cores on a single chip. It didn’t take long for researchers to realize that the architecture of a GPU is remarkably like that of a neural net.
Modern GPUs enabled the one-layer networks of the 1960s and the two- to three-layer networks of the 1980s to blossom into the 10-, 15-, even 50-layer networks of today. That’s what the “deep” in “deep learning” refers to — the depth of the network’s layers. And currently, deep learning is responsible for the best-performing systems in almost every area of artificial-intelligence research. – MIT Technology Review
A good video on neurons.
*https://www.youtube.com/watch?v=HZh0A-lWSmY
Reference #21: Article Titled:
Is AI a Term That Only Applies to Science Fiction Instead of Software?
https://bigthink.com/technology-innovation/why-a-i-is-a-big-fat-lie
https://www.forbes.com/sites/tomtaulli/2020/01/24/artificial-intelligence-ai-can-it-help-make-hollywood-blockbusters/#68e874272042
http://www.chilton-computing.org.uk/inf/literature/reports/lighthill_report/p004.htm
Reference #22: Article Titled:
Why You Should Have Your AI, Data Science Project Audited
Reference #23: Article Titled:
Why Your AI, Data Science Project Has Stalled
https://towardsdatascience.com/stop-hiring-data-scientists-30514028e202
https://medium.com/syncedreview/2019-in-review-10-ai-failures-317b46155350
https://www.theverge.com/2016/3/24/11297050/tay-microsoft-chatbot-racist
*https://betanews.com/2020/01/23/data-confidence-ai-failures/
Reference #24: Article Titled:
Is it True that AI is Going to Solve Global Warming?
https://www.microsoft.com/en-us/ai/ai-for-earth
*https://blogs.ei.columbia.edu/2018/06/05/artificial-intelligence-climate-environment/
https://docs.wixstatic.com/ugd/148cb0_a1406e0143ac4c469196d3003bc1e687.pdf
Reference #25: Article Titled:
When Will People Notice the Gap Between the Promise and Reality of AI?
*https://knowledge.wharton.upenn.edu/article/new-startup-aims-use-ai-amplify-humanity/
https://en.wikipedia.org/wiki/AI_accelerator
https://bigthink.com/technology-innovation/why-a-i-is-a-big-fat-lie
https://en.wikipedia.org/wiki/Animal_cognition
*https://www.visualcapitalist.com/ai-revolution-infographic/
https://www.edureka.co/blog/types-of-artificial-intelligence/
https://deepmind.com/
Reference #26: Article Titled:
How Much of Vishal Sikka’s Explanations on Artificial Intelligence is Complete BS?
https://en.wikipedia.org/wiki/AI_accelerator
https://en.wikipedia.org/wiki/Animal_cognition
*https://www.reddit.com/r/venturecapital/comments/eri5jl/the_reason_vcs_are_just_tired/
https://vishalsikka.blogspot.com/2015/12/openai-ai-for-all.html
Vishal Sikka: Why AI Needs a Broader, More Realistic Approach
Reference #27: Article Titled:
The Data Implications of AI and ML Projects
https://en.wikipedia.org/wiki/Labeled_data
https://www.techrepublic.com/article/is-data-labeling-the-new-blue-collar-job-of-the-ai-era/
https://stackoverflow.com/questions/19170603/what-is-the-difference-between-labeled-and-unlabeled-data/19172720#19172720
https://www.ant.works/
https://cdn2.hubspot.net/hubfs/3971219/Alegion_Supervised_vs_Unsupervised_Learning.pdf
https://towardsdatascience.com/four-mistakes-you-make-when-labeling-data-7e431c4438a2
https://medium.com/memory-leak/data-labeling-creating-ground-truth-44e64da6cc4f
*https://venturebeat.com/2019/06/12/essential-tips-for-scaling-quality-ai-data-labeling/
Reference #28: Article Titled:
Foresight Fall 2019 Issue Description
https://foresight.forecasters.org/product/foresight-issue-55/
Reference #29: Article Titled:
How Real is the SAP Machine Learning and Data Science Story?
https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
https://en.wikipedia.org/wiki/Statistical_classification
https://en.wikipedia.org/wiki/Logistic_regression
https://en.wikipedia.org/wiki/Cluster_analysis
Reference #30: Article Titled:
Did Hillary Lose the Election Due Failed Big Data and AI?
*https://www.amazon.com/dp/B07DPPM9C5/
Reference #31: Article Titled:
How Awful Was the Coverage of the McDonald’s AI Acquisition?
https://www.engadget.com/2019/03/26/mcdonalds-ai-drive-thru-machine-learning-dynamic-yield/
https://www.bbc.com/news/business-47722259
https://learn.g2.com/mcdonalds-ai-integration
*https://www.truthdig.com/articles/the-creepy-new-addition-to-mcdonalds-menu/
https://www.nrn.com/quick-service/mcdonald-s-automate-upselling-purchase-ai-company
https://www.barrons.com/articles/mcdonalds-stock-is-up-on-artificial-intelligence-buy-of-dynamic-yield-51553698431
https://www.forbes.com/sites/bernardmarr/2018/04/04/how-mcdonalds-is-getting-ready-for-the-4th-industrial-revolution-using-ai-big-data-and-robotics/#2ee585a73d33
https://www.eater.com/2015/4/13/8403905/52-percent-fast-food-workers-public-assistance-food-stamps-study
https://www.theatlantic.com/business/archive/2013/10/instead-living-wage-mcdonalds-tells-workers-sign-food-stamps/309625/
https://www.cnbc.com/2019/03/26/mcdonalds-300-million-deal-with-dynamic-yield-is-a-brilliant-move-for-artificial-intelligence-and-fast-food.html
https://gizmodo.com/mcdonalds-spent-300-million-on-its-own-version-of-the-1833634853
https://www.forbes.com/sites/forbesinsights/2019/03/31/mcdonalds-purchase-of-an-ai-company-goes-ways-beyond-just-do-you-want-fries-with-that/#2fe5a59d4d8d
https://www.wired.com/story/mcdonalds-big-data-dynamic-yield-acquisition/
*https://www.pymnts.com/news/partnerships-acquisitions/2019/mcdonalds-ai-personalization-company-dynamic-yield/
https://futurism.com/the-byte/mcdonalds-ai-dynamic-yield-predict
https://hospitalitytech.com/mcdonalds-tests-ai-powered-digital-menu-boards
https://www.pcmag.com/news/367447/mcdonalds-to-personalize-drive-thru-menus-using-ai
*https://www.smartcompany.com.au/startupsmart/news/mcdonalds-acquires-dynamic-yield/
https://diginomica.com/you-want-ai-with-that-mcdonalds-latest-tech-gambit-gets-highly-personal
*https://www.geek.com/tech/mcdonalds-drive-thru-gets-ai-upgrade-1780239/
https://futurism.com/the-byte/mcdonalds-ai-dynamic-yield-predict
https://phys.org/news/2019-04-mcdonald-significant-ai-fries.html
https://www.techspot.com/news/79374-mcdonald-latest-acquisition-bring-ai-drive-thru.html
*https://www.nationofchange.org/2019/07/30/big-macs-new-big-data-innovation/
https://www.ft.com/content/a1818006-4f4e-11e9-b401-8d9ef1626294
https://www.designnews.com/electronics-test/mcdonalds-putting-ai-its-drive-thrus/35639361260521
*https://news.crunchbase.com/news/mcdonalds-will-serve-artificial-intelligence-with-latest-300m-acquisition/
http://laborcenter.berkeley.edu/pdf/2015/the-high-public-cost-of-low-wages.pdf
Stagnating wages and decreased benefits are a problem not only for low-wage workers who increasingly cannot make ends meet, but also for the federal government as well as the 50 state governments that finance the public assistance programs many of these workers and their families turn to. Nearly three-quarters (73 percent) of enrollees in America’s major public sup-port programs are members of working families;4 the taxpayers bear a significant portion of the hidden costs of low-wage work in America.
Higher wages and increases in employer-provided health insurance would result in significant Medicaid savings that states and the federal government could apply to other programs and priorities.14 In the case of TANF—a block grant that includes maintenance of effort (MOE) provisions that require specified state spending—higher wages would allow states to reduce the portion of the program going to cash assistance while increasing the funding for other services such as child care, job train-ing, and transportation assistance.
Reference #32: Article Titled:
How Real is the Data Science Gap?
https://towardsdatascience.com/the-data-science-gap-5cc4e0d19ee3
*https://www.datanami.com/2017/12/12/deep-learning-may-not-deep/
*https://www.datanami.com/2019/07/15/big-data-is-still-hard-heres-why/
https://www.researchgate.net/figure/An-example-of-a-deep-neural-network-with-two-hidden-layers-The-first-layer-is-the-input_fig6_299474560
*https://www.datanami.com/2019/07/09/why-you-dont-need-ai/
*https://www.datanami.com/2019/06/24/hitting-the-reset-button-on-hadoop/
*https://www.datanami.com/2017/07/25/exposing-ais-1-problem/
*https://www.datanami.com/2017/03/13/hadoop-failed-us-tech-experts-say/
*https://venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production/
https://towardsdatascience.com/why-data-science-sucks-d4e0171aba46
*https://www.datanami.com/2019/04/17/data-scientist-title-evolving-into-new-thing/
*https://www.datanami.com/2019/05/01/three-ways-to-close-your-companys-data-science-skills-gap-now/
https://www.newyorker.com/magazine/2004/05/10/torture-at-abu-ghraib
*https://www.experfy.com/blog/this-is-why-your-data-scientist-sucks
*https://www.datanami.com/2019/06/10/hadoop-struggles-and-bi-deals-whats-going-on/
https://www.wsj.com/articles/data-challenges-are-halting-ai-projects-ibm-executive-says-11559035800*https://www.visualcapitalist.com/big-data-keeps-getting-bigger/
Reference #33: Article Titled:
The Next Big Thing in AI is to Excuse AI Failures
https://www.wsj.com/articles/data-challenges-are-halting-ai-projects-ibm-executive-says-11559035800
Reference #34: Article Titled:
Why AI and ML Are So Overrated for Forecasting
https://www.wsj.com/articles/data-challenges-are-halting-ai-projects-ibm-executive-says-11559035800
Reference #35: Article Titled:
How IBM is Distracting from the Watson Failure to Sell More AI
https://www.forbes.com/sites/adrianbridgwater/2019/06/04/ibm-injects-data-science-ai-into-its-db2-database/#239fa22d1d0a
https://gizmodo.com/why-everyone-is-hating-on-watson-including-the-people-w-1797510888
https://www.wsj.com/articles/ibm-bet-billions-that-watson-could-improve-cancer-treatment-it-hasnt-worked-1533961147
https://www.reuters.com/article/us-ibm-watson/ibm-to-invest-1-billion-to-create-new-business-unit-for-watson-idUSBREA0808U20140109
https://medium.com/syncedreview/2018-in-review-10-ai-failures-c18faadf5983
https://newrepublic.com/article/83337/ibm-watson-computer-jeopardy
*https://www.healthnewsreview.org/2017/02/md-anderson-cancer-centers-ibm-watson-project-fails-journalism-related/
Comment from this article:
“Watson’s win on Jeopardy wasn’t as straightforward as everyone thinks. Contrary to public perception, Watson has never had a speech interface. So for Jeopardy the questions were submitted in written form to Watson. However, the way the game was played, Watson received the question as soon as Alex Trebek began reading the question to the other contestants. With the speed that computers process information this meant that Watson had something like an hour to contemplate the question before the other contestants had finished hearing the last words. With this type of advantage it’s no surprise that Watson won. And IBM’s marketing department has taken that golden ring and run with it ever since.”
https://seekingalpha.com/article/4080310-artificial-intelligence-retrospective-analysis-ibm-2017-q1-earnings-call
https://www.wsj.com/articles/ibm-explores-sale-of-ibm-watson-health-11613696770
https://medcitynews.com/2021/02/will-ibms-reported-desire-to-sell-watson-cast-a-shadow-over-big-techs-healthcare-ambitions/?rf=1
https://medcitynews.com/2019/04/report-ibm-watson-for-drug-discovery/?rf=1
Additionally, in a 2017 interview with MedCity, a former IBM employee who worked in the company’s life sciences group explained that even though marketing budgets were large, the talk never materialized into a tangible off-the-shelf product. In the article, the employee said he has heard dissatisfaction from his former colleagues. “There’s a lot of frustration there. A lot of infighting and a lot of power jockeying and a lot of politics going on,” he said. “So people are getting fed up and leaving left and right.”
Last summer, IBM verified a round of layoffs impacted its Watson Health unit.
https://medcitynews.com/2017/09/former-ibm-employee-ai-truth-needs-come/?rf=1
“One of the big problems with any AI system is that you need data to train the algorithm. That is why radiology is a good application area–you can feed in thousands of examples where, say, a radiologist has found a tumor and soon the system learns to find tumors. The input data are well defined and the output states are limited,” said John Quackenbush, professor and director of the Center for Cancer Computational Biology, Dana-Farber Cancer Institute, in an email. “When you try to apply the system to medical decision making, it becomes more difficult. What is the right input data? Do you want to limit the analysis to a subset of the input data? And what are the right endpoint data? That becomes a challenge.”
Reference #36: Article Titled:
The Similarity Between Consulting Firms and Phone Sex Operators
https://www.accenture.com/_acnmedia/PDF-85/Accenture-Understanding-Machines-Explainable-AI.pdf#zoom=50
Reference #37: Article Titled:
How Many IBM and Other AI Projects Will Fail Due to a Lack of Data?
https://www.beckershospitalreview.com/artificial-intelligence/ibm-exec-says-data-related-challenges-are-biggest-reason-ai-projects-fall-through.html
*https://www.statnews.com/2018/06/11/ibm-watson-health-problems-layoffs/
*https://www.wraltechwire.com/2018/05/25/ugly-day-ibm-laying-off-workers-in-watson-health-group-including-triangle/
https://www.techrepublic.com/article/data-lakes-are-an-epic-fail-but-this-open-source-project-might-change-that/
*https://www.dataversity.net/is-it-time-to-drain-the-data-lake/#
https://www.theguardian.com/technology/2018/jul/06/artificial-intelligence-ai-humans-bots-tech-companies
We have reached an AI bubble to the point where we have AI “fraud.”
“It’s hard to build a service powered by artificial intelligence. So hard, in fact, that some startups have worked out it’s cheaper and easier to get humans to behave like robots than it is to get machines to behave like humans.
“Using a human to do the job lets you skip over a load of technical and business development challenges. It doesn’t scale, obviously, but it allows you to build something and skip the hard part early on,” said Gregory Koberger, CEO of ReadMe, who says he has come across a lot of “pseudo-AIs”.
“It’s essentially prototyping the AI with human beings,” he said.
In the case of the San Jose-based company Edison Software, artificial intelligence engineers went through the personal email messages of hundreds of users – with their identities redacted – to improve a “smart replies” feature. The company did not mention that humans would view users’ emails in its privacy policy.”
https://spectrum.ieee.org/biomedical/diagnostics/how-ibm-watson-overpromised-and-underdelivered-on-ai-health-care
“Outside of corporate headquarters, however, IBM has discovered that its powerful technology is no match for the messy reality of today’s health care system. And in trying to apply Watson to cancer treatment, one of medicine’s biggest challenges, IBM encountered a fundamental mismatch between the way machines learn and the way doctors work.
IBM’s bold attempt to revolutionize health care began in 2011. The day after Watson thoroughly defeated two human champions in the game of Jeopardy!, IBM announced a new career path for its AI quiz-show winner: It would become an AI doctor. IBM would take the breakthrough technology it showed off on television—mainly, the ability to understand natural language—and apply it to medicine. Watson’s first commercial offerings for health care would be available in 18 to 24 months, the company promised.
In fact, the projects that IBM announced that first day did not yield commercial products. In the eight years since, IBM has trumpeted many more high-profile efforts to develop AI-powered medical technology—many of which have fizzled, and a few of which have failed spectacularly. The company spent billions on acquisitions to bolster its internal efforts, but insiders say the acquired companies haven’t yet contributed much. And the products that have emerged from IBM’s Watson Health division are nothing like the brilliant AI doctor that was once envisioned: They’re more like AI assistants that can perform certain routine tasks.
But it also earned ill will and skepticism by boasting of Watson’s abilities. “They came in with marketing first, product second, and got everybody excited,” he says. “Then the rubber hit the road. This is an incredibly hard set of problems, and IBM, by being first out, has demonstrated that for everyone else.””
https://www.forbes.com/sites/jasonbloomberg/2017/07/02/is-ibm-watson-a-joke/#58e1cf23da20
“On the May 8th edition of Closing Bell on CNBC, venture capitalist Chamath Palihapitiya, founder and CEO of Social Capital, created quite a stir in enterprise artificial intelligence (AI) circles, when he took on IBMIBM +0% Watson, Big Blue’s AI platform.
“Watson is a joke, just to be completely honest,” Palihapitiya said. “I think what IBM is excellent at is using their sales and marketing infrastructure to convince people who have asymmetrically less knowledge to pay for something.””
An IBM partner contradicted this independent analyst.
“Not all bloggers sided with Palihapitiya, however. André M. König, Co-Founder at Opentopic (an IBM partner), added his two cents. “Well I agree that IBM is a formidable marketing machine, only to be outmatched by their corporate boldness and technological innovation,” König wrote. “If you call IBM Watson a joke you call the hundreds of companies and startups that have built on it a joke.””
The following addresses canceled Watson’s projects, a common feature of Watson.
“In February 2017, M.D. Anderson Cancer Center canceled a promising, but troubled contract with IBM for its Watson platform. “The breakup with M.D. Anderson seemed to show IBM choking on its own hype about Watson,” Freedman added. “The University of Texas, which runs M.D. Anderson, announced it had shuttered the project, leaving the medical center out $39 million in payments to IBM—for a project originally contracted at $2.4 million.
“After four years it had not produced a tool for use with patients that was ready to go beyond pilot tests.”
Moreover, despite significant progress, even state-of-the-art machine-learning algorithms often cannot deliver sufficient sensitivity, specificity, and precision (that is, positive predictive value) required for clinical decision making.”
Instead, IBM is ceding whatever AI leadership it purported to have to a new crop of far more innovative startups and other AI firms willing to reinvent themselves as the inexorable pace of innovation continues unabated – and that’s no joke.””
Which is the standard response, any partner of a vendor defends that vendor.
https://www.forbes.com/sites/tiriasresearch/2019/02/12/ibm-drives-watson-ai-everywhere/#529d9acb7ecc
https://thenextweb.com/artificial-intelligence/2018/06/13/what-happens-when-the-ai-bubble-bursts/
https://en.wikipedia.org/wiki/AI_winter
https://www.wsj.com/articles/data-challenges-are-halting-ai-projects-ibm-executive-says-11559035800
Reference #38: Article Titled:
The Problem with Machine Learning for Supply Chain Forecasting
*https://machinelearningmastery.com/introduction-to-time-series-forecasting-with-python/
*https://machinelearningmastery.com/machine-learning-with-python/
https://www.forbes.com/sites/stevebanker/2017/10/16/the-arms-race-to-leverage-machine-learning-in-supply-chain-planning/
https://en.wikipedia.org/wiki/Machine_learning
In our view, things like machine learning have gotten far too much attention, while the majority of companies in supply chain planning don’t know their forecast error.