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Ma­chine Learning

TagLast edit: 27 Nov 2020 16:38 UTC by Multicore

Machine Learning refers to the general field of study that deals with automated statistical learning and pattern detection by non-biological systems. It can be seen as a sub-domain of artificial intelligence that specifically deals with modeling and prediction through the knowledge extracted from training data. As a multi-disciplinary area, it has borrowed concepts and ideas from other areas like pure mathematics and cognitive science.

Understanding different machine learning algorithms

The most widely used distinction is between unsupervised (e.g. k-means clustering, principal component analysis) vs supervised (e.g. Support Vector Machines, logistic regression) methods. The first approach identifies interesting patterns (e.g. clusters and latent dimensions) in unlabeled training data, whereas the second takes labeled training data and tries to predict the label for unlabeled data points from the same distribution.

Another important distinction relates to the bias/​variance tradeoff—some machine learning methods are are capable of recognizing more complex patterns, but the tradeoff is that these methods can overfit and generalize poorly if there’s noise in the training data—especially if there’s not much training data available.

There are also subfields of machine learning devoted to operating on specific kinds of data. For example, Hidden Markov Models and recurrent neural networks operate on time series data. Convolutional neural networks are commonly applied to image data.

Applications

The use of machine learning has been widespread since its formal definition in the 50’s. The ability to make predictions based on data has been extensively used in areas such as analysis of financial markets, natural language processing and even brain-computer interfaces. Amazon’s product suggestion system makes use of training data in the form of past customer purchases in order to predict what customers might want to buy in the future.

In addition to its practical usefulness, machine learning has also offered insight into human cognitive organization. It seems likely machine learning will play an important role in the development of artificial general intelligence.

Further Reading & References

See Also

Pre­dic­tive Cod­ing has been Unified with Backpropagation

lsusr2 Apr 2021 21:42 UTC
135 points
42 comments2 min readLW link

An Illus­trated Proof of the No Free Lunch Theorem

lifelonglearner8 Jun 2020 1:54 UTC
17 points
0 comments1 min readLW link
(mlu.red)

Matt Botv­inick on the spon­ta­neous emer­gence of learn­ing algorithms

Adam Scholl12 Aug 2020 7:47 UTC
138 points
90 comments5 min readLW link

Us­ing GPT-N to Solve In­ter­pretabil­ity of Neu­ral Net­works: A Re­search Agenda

3 Sep 2020 18:27 UTC
60 points
11 comments2 min readLW link

the scal­ing “in­con­sis­tency”: openAI’s new insight

nostalgebraist7 Nov 2020 7:40 UTC
126 points
11 comments9 min readLW link
(nostalgebraist.tumblr.com)

I Trained a Neu­ral Net­work to Play Helltaker

lsusr7 Apr 2021 8:24 UTC
27 points
5 comments3 min readLW link

Neu­ral nets as a model for how hu­mans make and un­der­stand vi­sual art

Owain_Evans9 Nov 2019 16:53 UTC
28 points
7 comments2 min readLW link
(owainevans.github.io)

UML VI: Stochas­tic Gra­di­ent Descent

Rafael Harth12 Jan 2020 21:59 UTC
13 points
0 comments10 min readLW link

[Question] How do you do hy­per­pa­ram­e­ter searches in ML?

lsusr13 Jan 2020 3:45 UTC
9 points
3 comments1 min readLW link

Ma­chine Learn­ing Anal­ogy for Med­i­ta­tion (illus­trated)

abramdemski28 Jun 2018 22:51 UTC
85 points
48 comments1 min readLW link

Dis­cus­sion on the ma­chine learn­ing ap­proach to AI safety

Vika1 Nov 2018 20:54 UTC
25 points
3 comments4 min readLW link

UML IV: Lin­ear Predictors

Rafael Harth8 Jul 2020 19:06 UTC
13 points
0 comments9 min readLW link

Un­der­stand­ing Ma­chine Learn­ing (I)

Rafael Harth20 Dec 2019 18:22 UTC
44 points
11 comments11 min readLW link

Un­der­stand­ing Ma­chine Learn­ing (II)

Rafael Harth22 Dec 2019 18:28 UTC
24 points
4 comments10 min readLW link

Un­der­stand­ing Ma­chine Learn­ing (III)

Rafael Harth25 Dec 2019 18:55 UTC
16 points
0 comments11 min readLW link

UML V: Con­vex Learn­ing Problems

Rafael Harth5 Jan 2020 19:47 UTC
12 points
0 comments10 min readLW link

UML VII: Meta-Learning

Rafael Harth19 Jan 2020 18:23 UTC
14 points
0 comments15 min readLW link

UML VIII: Lin­ear Pre­dic­tors (2)

Rafael Harth26 Jan 2020 20:09 UTC
9 points
2 comments10 min readLW link

UML IX: Ker­nels and Boosting

Rafael Harth2 Feb 2020 21:51 UTC
13 points
1 comment10 min readLW link

A Sim­ple In­tro­duc­tion to Neu­ral Networks

Rafael Harth9 Feb 2020 22:02 UTC
32 points
8 comments18 min readLW link

UML XI: Near­est Neigh­bor Schemes

Rafael Harth16 Feb 2020 20:30 UTC
15 points
3 comments9 min readLW link

UML XII: Di­men­sion­al­ity Reduction

Rafael Harth23 Feb 2020 19:44 UTC
9 points
0 comments9 min readLW link

UML XIII: On­line Learn­ing and Clustering

Rafael Harth1 Mar 2020 18:32 UTC
13 points
0 comments14 min readLW link

UML final

Rafael Harth8 Mar 2020 20:43 UTC
22 points
1 comment14 min readLW link

[Link] Word-vec­tor based DL sys­tem achieves hu­man par­ity in ver­bal IQ tests

jacob_cannell13 Jun 2015 23:38 UTC
17 points
8 comments1 min readLW link

AlphaS­tar: Im­pres­sive for RL progress, not for AGI progress

orthonormal2 Nov 2019 1:50 UTC
111 points
58 comments2 min readLW link2 nominations1 review

Un­der­stand­ing “Deep Dou­ble Des­cent”

evhub6 Dec 2019 0:00 UTC
130 points
40 comments5 min readLW link3 nominations4 reviews

Let’s Read: Su­per­hu­man AI for mul­ti­player poker

Yuxi_Liu14 Jul 2019 6:22 UTC
56 points
6 comments8 min readLW link

OpenAI re­leases func­tional Dota 5v5 bot, aims to beat world cham­pi­ons by August

habryka26 Jun 2018 22:40 UTC
53 points
10 comments1 min readLW link
(blog.openai.com)

“The Bit­ter Les­son”, an ar­ti­cle about com­pute vs hu­man knowl­edge in AI

lahwran21 Jun 2019 17:24 UTC
50 points
14 comments4 min readLW link1 nomination
(www.incompleteideas.net)

[1911.08265] Mas­ter­ing Atari, Go, Chess and Shogi by Plan­ning with a Learned Model | Arxiv

DragonGod21 Nov 2019 1:18 UTC
52 points
4 comments1 min readLW link
(arxiv.org)

In­ter­pretabil­ity in ML: A Broad Overview

lifelonglearner4 Aug 2020 19:03 UTC
41 points
5 comments15 min readLW link

If I were a well-in­ten­tioned AI… I: Image classifier

Stuart_Armstrong26 Feb 2020 12:39 UTC
35 points
4 comments5 min readLW link

Search ver­sus design

alexflint16 Aug 2020 16:53 UTC
78 points
39 comments36 min readLW link

Con­cept Safety: Pro­duc­ing similar AI-hu­man con­cept spaces

Kaj_Sotala14 Apr 2015 20:39 UTC
49 points
45 comments8 min readLW link

in­ter­pret­ing GPT: the logit lens

nostalgebraist31 Aug 2020 2:47 UTC
106 points
28 comments10 min readLW link

“In­duc­tive Bias”

Eliezer Yudkowsky8 Apr 2007 19:52 UTC
30 points
24 comments3 min readLW link

Cross-Val­i­da­tion vs Bayesian Model Comparison

johnswentworth21 Jul 2019 18:14 UTC
22 points
2 comments4 min readLW link

Su­per­vised learn­ing of out­puts in the brain

Steven Byrnes26 Oct 2020 14:32 UTC
25 points
7 comments10 min readLW link

Does SGD Pro­duce De­cep­tive Align­ment?

Mark Xu6 Nov 2020 23:48 UTC
54 points
2 comments16 min readLW link

Mul­ti­modal Neu­rons in Ar­tifi­cial Neu­ral Networks

Kaj_Sotala5 Mar 2021 9:01 UTC
56 points
2 comments2 min readLW link
(distill.pub)

[Link] Whit­tle­stone et al., The So­cietal Im­pli­ca­tions of Deep Re­in­force­ment Learning

alenglander10 Mar 2021 18:13 UTC
11 points
1 comment1 min readLW link
(jair.org)

Opinions on In­ter­pretable Ma­chine Learn­ing and 70 Sum­maries of Re­cent Papers

9 Apr 2021 19:19 UTC
105 points
9 comments102 min readLW link

Place-Based Programming

lsusr14 Apr 2021 22:18 UTC
26 points
9 comments2 min readLW link

How can In­ter­pretabil­ity help Align­ment?

23 May 2020 16:16 UTC
33 points
3 comments9 min readLW link

GAN Discrim­i­na­tors Don’t Gen­er­al­ize?

tryactions8 Jun 2020 20:36 UTC
18 points
7 comments2 min readLW link

The “Out­side the Box” Box

Eliezer Yudkowsky12 Oct 2007 22:50 UTC
68 points
32 comments2 min readLW link

Us­ing ra­tio­nal­ity to de­bug Ma­chine Learning

Dr_Manhattan10 Apr 2018 20:03 UTC
20 points
3 comments1 min readLW link
(amid.fish)

Declar­a­tive Mathematics

johnswentworth21 Mar 2019 19:05 UTC
57 points
10 comments3 min readLW link

Ta­boo­ing ‘Agent’ for Pro­saic Alignment

Hjalmar_Wijk23 Aug 2019 2:55 UTC
50 points
9 comments6 min readLW link

Deep­Mind ar­ti­cle: AI Safety Gridworlds

scarcegreengrass30 Nov 2017 16:13 UTC
24 points
5 comments1 min readLW link
(deepmind.com)

Com­pet­i­tive Mar­kets as Distributed Backprop

johnswentworth10 Nov 2018 16:47 UTC
46 points
10 comments4 min readLW link

Ma­chine Learn­ing Pro­jects on IDA

24 Jun 2019 18:38 UTC
49 points
3 comments2 min readLW link

Mag­i­cal Categories

Eliezer Yudkowsky24 Aug 2008 19:51 UTC
57 points
132 comments9 min readLW link

Con­nec­tion­ism: Model­ing the mind with neu­ral networks

Scott Alexander19 Jul 2011 1:16 UTC
57 points
20 comments8 min readLW link

Deep learn­ing—deeper flaws?

Richard_Ngo24 Sep 2018 18:40 UTC
39 points
17 comments4 min readLW link
(thinkingcomplete.blogspot.com)

Sel­ling Nonapples

Eliezer Yudkowsky13 Nov 2008 20:10 UTC
46 points
78 comments7 min readLW link

[Question] What are the most im­por­tant pa­pers/​post/​re­sources to read to un­der­stand more of GPT-3?

adamShimi2 Aug 2020 20:53 UTC
22 points
4 comments1 min readLW link

[Link] Com­puter im­proves its Civ­i­liza­tion II game­play by read­ing the manual

Kaj_Sotala13 Jul 2011 12:00 UTC
49 points
5 comments4 min readLW link

Some thoughts af­ter read­ing Ar­tifi­cial In­tel­li­gence: A Modern Approach

swift_spiral19 Mar 2019 23:39 UTC
37 points
4 comments2 min readLW link

is gpt-3 few-shot ready for real ap­pli­ca­tions?

nostalgebraist3 Aug 2020 19:50 UTC
31 points
5 comments9 min readLW link
(nostalgebraist.tumblr.com)

Worse Than Random

Eliezer Yudkowsky11 Nov 2008 19:01 UTC
37 points
102 comments12 min readLW link

In­duc­tive bi­ases stick around

evhub18 Dec 2019 19:52 UTC
59 points
14 comments3 min readLW link

Rea­sons com­pute may not drive AI ca­pa­bil­ities growth

Kythe19 Dec 2018 22:13 UTC
42 points
10 comments8 min readLW link

Begin­ning Ma­chine Learning

crybx30 Apr 2018 15:54 UTC
12 points
4 comments6 min readLW link

Pro­saic AI alignment

paulfchristiano20 Nov 2018 13:56 UTC
40 points
4 comments8 min readLW link

[Question] Al­gorithms vs Compute

johnswentworth28 Jan 2020 17:34 UTC
26 points
11 comments1 min readLW link

Com­plex­ity Penalties in Statis­ti­cal Learning

michael_h6 Feb 2019 4:13 UTC
31 points
3 comments6 min readLW link

Mak­ing a Differ­ence Tem­pore: In­sights from ‘Re­in­force­ment Learn­ing: An In­tro­duc­tion’

TurnTrout5 Jul 2018 0:34 UTC
31 points
6 comments8 min readLW link

New pa­per: The In­cen­tives that Shape Behaviour

RyanCarey23 Jan 2020 19:07 UTC
23 points
5 comments1 min readLW link
(arxiv.org)

On AI and Compute

johncrox3 Apr 2019 19:00 UTC
36 points
10 comments8 min readLW link

Rein­ter­pret­ing “AI and Com­pute”

habryka25 Dec 2018 21:12 UTC
30 points
10 comments1 min readLW link
(aiimpacts.org)

The Ma­chine Learn­ing Per­son­al­ity Test

PhilGoetz4 Aug 2009 23:36 UTC
31 points
34 comments6 min readLW link

Op­ti­miz­ing a Week of Ma­chine Learn­ing Learning

Raemon9 Jan 2018 6:55 UTC
8 points
2 comments3 min readLW link

Alex Ir­pan: “My AI Timelines Have Sped Up”

Vaniver19 Aug 2020 16:23 UTC
43 points
20 comments1 min readLW link
(www.alexirpan.com)

“De­sign­ing agent in­cen­tives to avoid re­ward tam­per­ing”, DeepMind

gwern14 Aug 2019 16:57 UTC
28 points
15 comments1 min readLW link
(medium.com)

Mas­ter­ing Chess and Shogi by Self-Play with a Gen­eral Re­in­force­ment Learn­ing Algorithm

DragonGod6 Dec 2017 6:01 UTC
13 points
4 comments1 min readLW link
(arxiv.org)

Why Gra­di­ents Van­ish and Explode

Matthew Barnett9 Aug 2019 2:54 UTC
25 points
9 comments3 min readLW link

Ex­am­ples of AI’s be­hav­ing badly

Stuart_Armstrong16 Jul 2015 10:01 UTC
41 points
37 comments1 min readLW link

Speci­fi­ca­tion gam­ing ex­am­ples in AI

Samuel Rødal10 Nov 2018 12:00 UTC
24 points
6 comments1 min readLW link
(docs.google.com)

Learn­ing with catastrophes

paulfchristiano23 Jan 2019 3:01 UTC
27 points
9 comments4 min readLW link

LDL 7: I wish I had a map

magfrump30 Nov 2017 2:03 UTC
13 points
2 comments3 min readLW link

Self-Su­per­vised Learn­ing and AGI Safety

Steven Byrnes7 Aug 2019 14:21 UTC
28 points
9 comments12 min readLW link

LDL 2: Non­con­vex Optimization

magfrump20 Oct 2017 18:20 UTC
13 points
13 comments4 min readLW link

Which of these five AI al­ign­ment re­search pro­jects ideas are no good?

rmoehn8 Aug 2019 7:17 UTC
25 points
13 comments1 min readLW link

Model splin­ter­ing: mov­ing from one im­perfect model to another

Stuart_Armstrong27 Aug 2020 11:53 UTC
42 points
9 comments33 min readLW link

Tech­ni­cal model re­fine­ment formalism

Stuart_Armstrong27 Aug 2020 11:54 UTC
9 points
0 comments6 min readLW link

Pong from pix­els with­out read­ing “Pong from Pix­els”

naimenz29 Aug 2020 17:26 UTC
15 points
1 comment7 min readLW link

Us­ing ma­chine learn­ing to pre­dict ro­man­tic com­pat­i­bil­ity: em­piri­cal results

JonahS17 Dec 2014 2:54 UTC
37 points
18 comments11 min readLW link

Log­i­cal or Con­nec­tion­ist AI?

Eliezer Yudkowsky17 Nov 2008 8:03 UTC
26 points
26 comments9 min readLW link

Ar­tifi­cial In­tel­li­gence and Life Sciences (Why Big Data is not enough to cap­ture biolog­i­cal sys­tems?)

HansNauj15 Jan 2020 1:59 UTC
6 points
3 comments6 min readLW link

LDL 4: Big data is a pain in the ass

magfrump25 Oct 2017 20:59 UTC
6 points
0 comments3 min readLW link

“Learn­ing to Sum­ma­rize with Hu­man Feed­back”—OpenAI

Rekrul7 Sep 2020 17:59 UTC
57 points
2 comments1 min readLW link

My (Mis)Ad­ven­tures With Al­gorith­mic Ma­chine Learning

AHartNtkn20 Sep 2020 5:31 UTC
14 points
4 comments41 min readLW link

[Question] Why isn’t JS a pop­u­lar lan­guage for deep learn­ing?

Will Clark8 Oct 2020 14:36 UTC
12 points
21 comments1 min readLW link

[Question] GPT-3 + GAN

stick10917 Oct 2020 7:58 UTC
4 points
2 comments1 min readLW link

Per­cep­trons Explained

lifelonglearner14 Feb 2020 17:34 UTC
13 points
2 comments1 min readLW link
(owenshen24.github.io)

The Weighted Ma­jor­ity Algorithm

Eliezer Yudkowsky12 Nov 2008 23:19 UTC
25 points
96 comments10 min readLW link

If Van der Waals was a neu­ral network

George28 Jan 2020 18:38 UTC
18 points
3 comments11 min readLW link
(blog.cerebralab.com)

“model scores” is a ques­tion­able concept

Maxwell Peterson6 Nov 2020 3:19 UTC
26 points
0 comments6 min readLW link

Fre­quen­tist prac­tice in­cor­po­rates prior in­for­ma­tion all the time

Maxwell Peterson7 Nov 2020 20:43 UTC
17 points
0 comments4 min readLW link

Model Depth as Panacea and Obfuscator

abstractapplic9 Nov 2020 0:02 UTC
8 points
3 comments15 min readLW link

[Question] Can this model grade a test with­out know­ing the an­swers?

Elizabeth31 Aug 2019 0:53 UTC
20 points
3 comments1 min readLW link

Re­think­ing Batch Normalization

Matthew Barnett2 Aug 2019 20:21 UTC
20 points
5 comments8 min readLW link

Link: In­ter­view with Vladimir Vapnik

Daniel_Burfoot25 Jul 2009 13:36 UTC
22 points
6 comments2 min readLW link

[Linkpost] AlphaFold: a solu­tion to a 50-year-old grand challenge in biology

adamShimi30 Nov 2020 17:33 UTC
54 points
22 comments1 min readLW link
(deepmind.com)

Min­i­mal Maps, Semi-De­ci­sions, and Neu­ral Representations

Zachary Robertson6 Dec 2020 15:15 UTC
30 points
2 comments4 min readLW link

Ma­chine learn­ing could be fun­da­men­tally unexplainable

George16 Dec 2020 13:32 UTC
25 points
15 comments15 min readLW link
(cerebralab.com)

The case for al­ign­ing nar­rowly su­per­hu­man models

Ajeya Cotra5 Mar 2021 22:29 UTC
167 points
72 comments38 min readLW link

The Ja­panese Quiz: a Thought Ex­per­i­ment of Statis­ti­cal Epistemology

DanB8 Apr 2021 17:37 UTC
8 points
0 comments9 min readLW link
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