The term Artificial Intelligence was originally coined by John McCarthy in 1955, defining it as “the science and engineering of making intelligent machines”. Now more than a half a century old, the field of AI and machine learning is finally achieving some of its oldest goals by being used successfully in areas such as data mining, industrial robotics, logistics, speech recognition, banking software, medical diagnosis and search engines.

Tech giants have all been investing heavily in AI and Machine Learning. In 2010 Facebook introduced facial recognition technology, and in 2013 Mark Zuckerberg dedicated a lab to AI research. In 2014 Google bought artificial intelligence startup DeepMind for $400 million (£263 million), making it one of the largest tech acquisitions to date. Microsoft have also been investing heavily in AI with their project Oxford, which uses an emotion detection service that can assign an emotion to a person depending on their facial expression. This kind of facial recognition allows photos to be edited depending on the feelings expressed in them.

IBM have also been making large strides in AI and Machine learning with their Watson computer which famously won the US quiz show Jeopardy in 2011, outperforming his human counterparts. IBM and have now teamed up with Nvidia incorporating Nvidia’s Tesla K80 GPUs, making Watson 1.7 faster at responding to inquiries. IBM are also developing a teaching assistant app that will plan lessons based on approved material.

Apple has bought artificial intelligence startup Emotient and while it’s not completely clear what Apple’s plans are, reports suggest that the acquisition will centre on facial recognition technology and customers reaction to ads. Apple have also acquired UK-based AI company Vocal IQ with reports suggesting that they are aiming to develop Siri further and use Vocal IQ’s speech AI software.

From Q1 to Q3 2015 we saw $47.2 billion invested in AI and Machine Learning, and with roughly 900 companies working in the AI field tackling problems in business intelligence, finance and security, the centuries’ long quest to develop machines and software with human-like intelligence inches closer to reality.

WHAT ARE THE EXPERTS SAYING?

We reached out to some of the top 20 influencers to ask them for their views on artificial intelligence and machine learning. We spoke to Mike Tamir (#1), Kirk Borne (#2), Gregory Piatetsky (#3), Gene Kogan (#7) and Randal S. Olson (#10). Be sure to follow them to stay up to date on the best content and resources on artificial intelligence and machine learning!


Onalytica - Artificial Intelligence and Machine Learning Top 100 Influencers and Brands - Mike TamirMike Tamir Ph.D. – Chief Science Officer / Chief Learning Officer at Galvanize

“We are at an important moment in the history of AI and Machine Learning. The 20th century is littered with false starts and failures to apply rule based feedback to help machines linguistically interact with humans.  Recent machine learning driven advances in how we represent text data with continuous neural word embedding have changed the game.  Techniques such as word2vec have enabled us to densely represent natural language data for deep learning applications capable of successfully classifying the semantic relationships between concepts and and even inferential relationships between sentences.  This success coupled with similar successes with deep learning convo-nets when it comes to image recognition have potentially brought us to the precipice of a “Deep Blue” moment for challenging problems like the “Turing Test” that have daunted AI for decades.”


Onalytica - Big Data Top 100 Influencers and Brands Kirk D BorneKirk Borne – Principal Data Scientist at Booz Allen

“People sometimes ask “how is machine learning different from data mining and AI?” I say that machine learning refers to the basis set of mathematical algorithms that learn the models that describe the patterns and features in data, while data mining is the application of those algorithms for making discoveries from large data sets, and Artificial Intelligence is the application of those algorithms for autonomous action and decision-making within devices (such as robotics). I would now combine all of those concepts under the umbrella of data science. Furthermore, the emergence of deep learning, fast machine learning technologies (such as GPUs), and modular architectures (such as Raspberry Pi) is now blurring the distinction between data mining and AI. Actionable intelligence is no longer just a business buzzword — it is a business imperative.”


Onalytica - Big Data 2016 Top 100 Influencers and Brands - Gregory Piatetsky

Gregory Piatetsky – President of KDnuggets

“Machine Learning is at the core of AI. Machine Learning and especially Deep Learning, enabled by Big Data, fast GPUs, and better algorithms, is already achieving human or superhuman performance in many areas, including game playing, image recognition, and speech understanding. Past “experts” who said that AI will not defeat human chess master, or human chess champion, or will not recognize images, etc, have all been proven wrong. There do not seem to be any barriers to what AI and Machine Learning can achieve. Now is the time to take AI seriously and consider immense benefits but also huge risks from a human-level AI.”


Onalytica - Artificial Intelligence and Machine Learning Top 100 Influencers and Brands - Gene KoganGene Kogan – Researcher at Sourcetone, Engineer / Instructor at Harvestworks

“I’m excited about machine learning’s applications for human creativity and expressivity, and would like to see more resources invested in democratizing access to research and education.”


Onalytica - Artificial Intelligence and Machine Learning Top 100 Influencers and Brands - Randy OlsonRandal S. Olson – Postdoctoral Researcher at the University of Pennsylvania Institute for Biomedical Informatics

“With DeepMind’s AlphaGO taking on a world expert Go player, Boston Dynamics’ bipedal robot (Atlas) marching into the robotics scene, and so much more, 2016 is looking to be a groundbreaking year for artificial intelligence and machine learning. I look forward to reporting on the latest in AI & ML as we advance the state-of-the-art of what machines can accomplish.”


MAPPING THE COMMUNITY

We were keen to see which Artificial Intelligence and Machine Learning brands and individuals were leading the online discussion. So we analysed 1.1M+ tweets from 30 Novemver 2015 – 24 February 2016 mentioning the keywords “#AI OR “Artificial Intelligence” OR ArtificialIntelligence OR “Machine Learning” OR Machinelearning” and identified the top 100 most influential brands and individuals leading the discussion on Twitter. What we discovered was a very engaged community, with much discussion between individuals and brands. Below you can see a network map of the top 100 engaged users in the Artificial Intelligence and Machine Learning conversation. This map was created with our Influencer Relationship Management software (IRM). Be sure to click on the map to enjoy the full size network diagram in greater detail.

Onalytica - Artificial Intelligence and Machine Learning Top 100 Influencers and Brands - Network Map

 

Below you can see another network map created with our Influencer Relationship Management software (IRM) showing the #2 Influencer Kirk Borne, and the conversations to and from the different influencers in his field. If you are interested in learning more about identifying, managing and engaging with influencers click here to get in touch!

Onalytica - Artificial Intelligence and Machine Learning Top 100 Influencers and Brands - Network Map (Kirk Borne)

TOP 100 INDIVIDUALS

We looked at all the individuals engaging on Twitter to bring you a list of the top 100 most influential individuals in the Artificial Intelligence and Machine Learning conversation. Be sure to download the report to see who ranked beyond 50 on the list.

Download The Full Report

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Rank Twitter Handle Name Company Influencer Score
1 @MikeTamir Michael Tamir Galvanize 42.37
2 @KirkDBorne Kirk Borne Booz Allen Hamilton 41.5
3 @kdnuggets Gregory Piatetsky KDNuggets 36.87
4 @craigbrownphd Craig Brown, Ph.D. STEM 32.61
5 @bobgourley Bob Gourley Cognitio Corp 23.73
6 @davidwkenny David Kenny IBM Watson 23.49
7 @genekogan Gene Kogan Sourcetone 23.18
8 @randhindi Rand Hindi Snips 22.87
9 @mappingbabel Jack Clark Bloomberg 17.51
10 @randal_olson Randy Olson University of Pennsylvania 17.03
11 @jordannovet Jordan Novet VentureBeat 16.92
12 @GaryMarcus Gary Marcus Geometric Intelligence 16.53
13 @xamat Xavier Amatriain Quora 15.69
14 @RickKing16 Rick King Consultant 14.95
15 @EdKwedar Ed Kwedar Consultant 14.21
16 @miha_jlo Mihajlo Grbovic Yahoo Labs 13.62
17 @ilparone Jarno M. Koponen Random, TechCrunch 13.16
18 @alexjc Alex J. Champandard AiGameDev, nucl.ai 12.35
19 @guestrin Carlos Guestrin Dato 11.57
20 @willknight will knight Technology Review 9.73
21 @hannawallach Hanna Wallach Microsoft 9.71
22 @TamaraMcCleary Tamara McCleary Relationshift 9.63
23 @AndrewYNg Andrew Ng Baidu, Coursera 9.14
24 @erikbryn Erik Brynjolfsson MIT 8.61
25 @jshieber Jonathan Shieber TechCrunch 8.47
26 @MFordFuture Martin Ford Author, Keynote Speaker 8.42
27 @etzioni Oren Etzioni AIlen Institute for AI 8.35
28 @simonlporter SimonPorter IBM 8.06
29 @pmddomingos Pedro Domingos University of Washington 7.49
30 @josephsirosh Joseph Sirosh Microsoft 7.28
31 @RebeccaFiebrink Rebecca Fiebrink Goldsmiths 7.07
32 @YvesMulkers Yves Mulkers 7wdata 6.89
33 @DennisMortensen Dennis R. Mortensen x.ai 6.35
34 @tsimonite Tom Simonite Technology Review 6.34
35 @ITredux Theo Priestley ITredux, Microsoft 6.3
36 @EvanSinar Evan Sinar DDI 6.29
37 @Wikisteff Steffen Christensen Horizons 6.25
38 @bobehayes Bob E. Hayes, PhD AnalyticsWeek 6.05
39 @gigastacey Stacey Higginbotham IoT Podcast 5.76
40 @clarecorthell Clare Corthell Luminant Data 5.62
41 @MLnick Nick Pentreath Graphflow 5.6
42 @reckel Richard Eckel Microsoft 5.56
43 @harryshum harryshum Microsoft 5.43
44 @nanettebyrnes Nanette Byrnes Technology Review 5.4
45 @DiegoKuonen Dr. Diego Kuonen Statoo Consulting 5.37
46 @robertoglezcano Roberto A. González San Pablo University 5.35
47 @TeachTheMachine Jason Brownlee Clever Computations 5.29
48 @mpshanahan Murray Shanahan Imperial College 5.22
49 @ManjuManglani Manju Manglani managingpartner 5.21
50 @bigdata Ben Lorica O’Reilly Media 5.18

 

TOP 100 BRANDS

In the top 100 brands we found a great selection of organisations such as IBM, Microsoft, Nvidia and Yahoo Labs as well as some top industry resources such as TechCrunch and MIT Tech Review. Be sure to download the full report to see who ranked from 50-100.

Download The Full Report

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Rank Twitter Handle Name Influencer Score
1 @xdotai x.ai 100
2 @wef World Economic Forum 76.43
3 @IBMWatson IBM Watson 70.41
4 @xprize XPRIZE 68.77
5 @TechCrunch TechCrunch 46.66
6 @MSFTResearch Microsoft Research 45.93
7 @IBM IBM 38.03
8 @Microsoft Microsoft 37.16
9 @techreview MIT Tech Review 36.4
10 @Forbes Forbes 33.16
11 @nvidia NVIDIA 30.94
12 @Techmeme Techmeme 27.41
13 @analyticbridge Big Data Science 26.24
14 @sejournal SearchEngineJournal® 26.17
15 @YahooLabs Yahoo Labs 19.13
16 @Toyota Toyota USA 18.59
17 @innovateuk Innovate UK 18.55
18 @TechRepublic TechRepublic 15.29
19 @singularityhub Singularity Hub 15.22
20 @open_ai OpenAI 15.14
21 @IBMResearch IBM Research 14.86
22 @AtlasFuture Atlas of the Future 14.5
23 @teamrework RE.WORK 14.11
24 @FLIxrisk Future of Life 13.98
25 @engadget Engadget 13.75
26 @ML_toparticles Machine Learning 12.19
27 @DataScienceCtrl Data Science Central 11.71
28 @VentureBeat VentureBeat 11.55
29 @MSFTVentures Microsoft Ventures 11.42
30 @deeplearning4j deeplearning4j 10.8
31 @TensorFlo TensorFlow 9.52
32 @InfoQ InfoQ 9.32
33 @SwanseaUni Swansea University 9.25
34 @developerWorks IBM developerWorks 8.9
35 @AlchemyAPI AlchemyAPI 8.76
36 @MaluubaInc Maluuba 8.68
37 @BigCloudTeam Big Cloud 8.07
38 @broadinstitute Broad Institute 7.84
39 @snips Snips 7.49
40 @DataconomyMedia Dataconomy 7.22
41 @declara Declara 7.09
42 @Azure Microsoft Azure 7.06
43 @ITPro IT Pro 6.92
44 @IBM_NEWS IBM News Room 6.91
45 @datoinc Dato 6.62
46 @Datafloq Datafloq 5.8
47 @AnalyticsVidhya Analytics Vidhya 5.6
48 @hseas Harvard SEAS 5.58
49 @Recode Re/code 5.21
50 @royalsociety The Royal Society 5.1

 

At Onalytica we love building these lists and want to give back to our loyal readers as much as we can. If you’re interested in other topics (such as Machine to Machine, Wearable TechInternet of Things) be sure to have a gander on our blog or why not propose some topics to us on twitter? We also build some very cool software to manage all of these influencers. Get a free demo today by clicking the button below!

REQUEST A FREE DEMO

Disclaimer: As ever with these lists, it must be stressed that the ranking is by no means a definitive measurement of influence, as there is no such thing. The brands and individuals listed are undoubtedly influential when it comes to driving discussion in the Artificial Intelligence and Machine Learning debate.

The PageRank based methodology we use to extract influencers on a particular topic takes into account the number and quality of contextual references that a user receives. These calculations are independent of a user’s number of followers, but we do filter our lists based on how much a user is engaged in the conversation and the influence they drive through their networks.

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