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The Future of Training and Fun: Accushoot LFX [video]

youtube.com

1 ポイント投稿者 aficionado2 年前0 コメント

[untitled]

1 ポイント投稿者 aficionado3 年前0 コメント

Digitizing Marksmanship with AccuShoot: A Fresh Take in an Age-Old Industry

blog.accushoot.com

2 ポイント投稿者 aficionado3 年前0 コメント

Will ChatGPT help retire me as Software Engineer anytime soon?

blog.bigml.com

1 ポイント投稿者 aficionado3 年前1 コメント

Machine Learning Operations Made Easy

blog.bigml.com

2 ポイント投稿者 aficionado4 年前0 コメント

Object Detection: Some Gory Details

blog.bigml.com

4 ポイント投稿者 aficionado4 年前0 コメント

Image Processing: The Simple and the Complex

blog.bigml.com

14 ポイント投稿者 aficionado5 年前1 コメント

What makes BigML Image Processing different

blog.bigml.com

8 ポイント投稿者 aficionado5 年前0 コメント

Don’t Become a Data Scientist

towardsdatascience.com

16 ポイント投稿者 aficionado6 年前0 コメント

How to fight Covid-19 with machine learning

towardsdatascience.com

1 ポイント投稿者 aficionado6 年前0 コメント

Machine Learning Benchmarking: You’re Doing It Wrong

blog.bigml.com

3 ポイント投稿者 aficionado6 年前0 コメント

Jaime Carbonell, machine translation pioneer, has died

cmu.edu

28 ポイント投稿者 aficionado6 年前1 コメント

Deep Learning Is Not as Deep as You Think

blog.bigml.com

3 ポイント投稿者 aficionado7 年前0 コメント

Build Your Own Machine Learning Models to Predict the 2019 Oscars

blog.bigml.com

1 ポイント投稿者 aficionado7 年前0 コメント

Comparing Feature Selection Scripts

blog.bigml.com

2 ポイント投稿者 aficionado7 年前0 コメント

Powering the Next Wave of Intelligent IoT Devices with Machine Learning – Part 1

blog.bigml.com

3 ポイント投稿者 aficionado7 年前0 コメント

Predictions Scorecard

rodneybrooks.com

28 ポイント投稿者 aficionado8 年前4 コメント

Predictions Scorecard

rodneybrooks.com

3 ポイント投稿者 aficionado8 年前0 コメント

Learning from data to predict future symptoms of oncology patients

journals.plos.org

1 ポイント投稿者 aficionado8 年前0 コメント

Better medicine through machine learning: What’s real, and what’s artificial?

journals.plos.org

2 ポイント投稿者 aficionado8 年前0 コメント

コメント

5 年前議論

Why are they regulating only AI? They should wide the spectrum of the regulation and include, for example, graph theory, combinatorics, probability theory, etc and any other branch of discrete mathematics or even mathematics and statistics in general. They look like pretty good tools to perpetrate those crimes too. Or, wait, are those “practices” even classified as crimes but current laws? How many people and organizations were fined or incarcerated for those last year? Let’s suppose they are. If someone commits one of those crimes hardcoding a system or simply using some random generator, or simply sending handwritten letters will this regulation apply? I really wonder how many AI-based systems the people proposing this regulation have really built.

7 年前議論

Links to dataset do not work.

9 年前議論

Ideal to practice, learn, and teach machine learning:

https://bigml.com/ml101 https://bigml.com/education/videos

9 年前議論

https://bigml.com/education/videos https://bigml.com/ml101/ https://bigml.com/tutorials/

9 年前議論

This is one of the many desperate moves that we will see from IBM trying to deliver on the Watson overpromise and all their rocambolesque cognitive computing. AI is a late project (https://en.wikipedia.org/wiki/The_Mythical_Man-Month). So adding more manpower (or brainpower) to a late software project only makes it later. Only folks poorly educated on the topic (such as Elon Musk) really believe that Watson-like AI is around the corner.

10 年前議論

The solution is easy and it applies to most sciences: all research articles should include a pointer to download the dataset that was used and an annex with the details on how it was collected.

10 年前議論

Basically this video ignores the history of machine learning in general. Jumping from Expert Systems to Neural Networks and Deep Learning is actually ignoring 36 years (and billions of dollars) of research http://machinelearning.org/icml.html (Breiman, Quinlan, Mitchell, Dietterich, Domingos, etc). Calling 2012 the seminal moment of Deep Learning is quite hard to digest. Maybe it means that 2012 is the point in time when the VC community discovered machine learning? Even harder to digest is calling Deep Learning the most productive and accurate machine learning system. What about more business oriented domains (without unstructured inputs), the extreme difficulties and expertise required to fine tune a network for a specific problem, or some drawbacks like the ones explained by http://arxiv.org/pdf/1412.1897v2.pdf or http://cs.nyu.edu/~zaremba/docs/understanding.pdf.

Those who ignore history are doomed to repeat it. As Roger Schank pointed out recently http://www.rogerschank.com/fraudulent-claims-made-by-IBM-abo..., another AI winter is coming soon! Funny that the video details the three first AI winters but the author doesn't realize that this excessive enthusiasm in one particular technique is contributing to a new one!

11 年前議論

Don't think so. The data and how it's represented is the code. ML is the compiler.

11 年前議論

How many elevators were failing today? how many were fixed today? and how many have been monitored in real-time to get fixed as they fail? Likewise, statistical models can be monitored and fixed automatically, and, of course, even so they will fail from time to time like elevators do.

11 年前議論

I'm a little bit worried. At least at the same level as when I see a bunch of developers compiling programs without much understanding of what an LL(k) parser does, or how a pushdown automaton works, or what a Turing machine is. I usually feel the same every time I see an elevator without a liftman, don't compute a square root by hand, or hear about Google self-driving cars.

11 年前議論

Did anyone actually give it a try? I only get this error with any dataset (even a humble Iris): Amazon ML cannot create an ML model: 1 validation error detected: Value null at 'predictiveModelType' failed to satisfy constraint: Member must not be null

11 年前議論

If you use BigML's web forms, the first request caches the model locally so that all the subsequent predictions are performed directly in your browser.

11 年前議論

No... it's Amazon ML and Azure ML trying to catch up with BigML. They copied many things from our service but forgot to copy the ease of use. Services like Azure ML, Amazon ML and even Google Predict API work like a black box, and lock your model away, making you extremely dependent on their proprietary service. With BigML, you can easily export your models and use them anywhere for free. If the goal is to democratize machine learning, then the ability to extract your models and use them as you see fit is essential, and only BigML offers that level of freedom.

11 年前議論

BigML cofounder here. Most BigML customers doing machine learning at scale use either BigML subscriptions (starting $30/mo) or private deployments – both of which provide unlimited model training and predictions and are suitable for developers and large enterprises alike. In addition, with BigML you can export your models (for cluster analysis and anomaly detection and not just classification/regression) to run locally and/or to be incorporated in related systems and services.

12 年前議論

fully agree! seen and still see it every day!

13 年前議論

In this particular case, I think that you are right. But isn't it powerful to have it at the command line level? It's great to quickly create models as you can also use them to make predictions at the command line level.

13 年前議論

A very powerful command line tool for Machine Learning: http://bigmler.readthedocs.org/en/latest/

Create a predictive model is as simple as: bigmler --train < data.csv

or create a predictive model for Bitcoin volume taking online data from Quandl and parsing it with jq in just one line.

curl --silent "http://www.quandl.com/api/v1/datasets/BITCOIN/BTCDEEUR.json" | jq -c ".data" | bigmler --train --field-attributes <(count=0; for i in `curl --silent "http://www.quandl.com/api/v1/datasets/BITCOIN/BTCDEEUR.json" | jq -c ".column_names[]"`; do echo "$count, $i"; count=$[$count+1]; done) --name bitcoin

More info here: http://blog.bigml.com/2013/01/31/fly-your-ml-cloud-like-a-ki...

14 年前議論

I used Megafauna Software parsed data to quickly create a model using BigML: http://bml.io/W6lKqe It uses information gain mix to create a decision tree. It also concluded that winning the 'best director' is the most important field. However, 'make up' didn't top the most important fields. See the results below.

    Data distribution:
    n: 16.94% (82 instances)
    y: 83.06% (402 instances)

    Predicted distribution:
    n: 16.74% (81 instances)
    y: 83.26% (403 instances)

    Field importance:
    1. Directing_1: 84.47%
    2. Music (Scoring)_0: 3.59%
    3. Music (Scoring)_1: 2.68%
    4. Art Direction_0: 2.39%
    5. Sound_0: 1.75%
    6. Film Editing_0: 1.55%
    7. Costume Design_1: 1.40%
    8. Film Editing_1: 0.82%
    9. Costume Design_0: 0.54%
    10. Cinematography_1: 0.52%
    11. Art Direction_1: 0.29%
    12. Special Achievement Award_0: 0.00%
    13. Scientific and Technical (Bonner Medal)_1: 0.00%
    14. Foreign Language Film_0: 0.00%
    15. Sound Editing_1: 0.00%
    16. Scientific and Technical (Bonner Medal)_0: 0.00%
    17. Sound Editing_0: 0.00%
    18. Scientific and Technical (Academy Award of Merit)_1: 0.00%
    19. Sound_1: 0.00%
    20. Animated Feature Film_1: 0.00%
    21. Scientific and Technical (Academy Award of Merit)_0: 0.00%
    22. Documentary (other)_1: 0.00%
    23. Animated Feature Film_0: 0.00%
    24. Music (Song)_1: 0.00%
    25. Documentary (other)_0: 0.00%
    26. Actress -- Supporting Role_1: 0.00%
    27. Short Film (Live Action)_1: 0.00%
    28. Music (Song)_0: 0.00%
    29. Documentary (Short Subject)_1: 0.00%
    30. Actress -- Supporting Role_0: 0.00%
    31. Short Film (Live Action)_0: 0.00%
    32. Documentary (Short Subject)_0: 0.00%
    33. Actress -- Leading Role_1: 0.00%
    34. Short Film (Animated)_1: 0.00%
    35. Documentary (Feature)_1: 0.00%
    36. Short Film (Animated)_0: 0.00%
    37. Makeup_1: 0.00%
    38. Scientific and Technical (Technical Achievement Award)_1: 0.00%
    39. Actress -- Leading Role_0: 0.00%
    40. Documentary (Feature)_0: 0.00%
    41. Actor -- Supporting Role_1: 0.00%
    42. Makeup_0: 0.00%
    43. Actor -- Supporting Role_0: 0.00%
    44. Scientific and Technical (Technical Achievement Award)_0: 0.00%
    45. Jean Hersholt Humanitarian Award_1: 0.00%
    46. Directing_0: 0.00%
    47. Actor -- Leading Role_1: 0.00%
    48. Scientific and Technical (Special Awards)_1: 0.00%
    49. Jean Hersholt Humanitarian Award_0: 0.00%
    50. Actor -- Leading Role_0: 0.00%
    51. Writing_1: 0.00%
    52. Scientific and Technical (Special Awards)_0: 0.00%
    53. Irving G. Thalberg Memorial Award_1: 0.00%
    54. Acting (other)_1: 0.00%
    55. Writing_0: 0.00%
    56. Scientific and Technical (Scientific and Engineering Award)_1: 0.00%
    57. Irving G. Thalberg Memorial Award_0: 0.00%
    58. Acting (other)_0: 0.00%
    59. Visual Effects_1: 0.00%
    60. Scientific and Technical (Scientific and Engineering Award)_0: 0.00%
    61. Honorary Award_1: 0.00%
    62. Cinematography_0: 0.00%
    63. Visual Effects_0: 0.00%
    64. Scientific and Technical (Gordon E. Sawyer Award)_1: 0.00%
    65. Honorary Award_0: 0.00%
    66. Category_1: 0.00%
    67. Special Achievement Award_1: 0.00%
    68. Scientific and Technical (Gordon E. Sawyer Award)_0: 0.00%
    69. Foreign Language Film_1: 0.00%
    70. Category_0: 0.00%

14 年前議論

Great stuff!

14 年前議論

Great news!!!