Intelligence Artificielle

Opportunités et défis (pour les développeurs, entre autre)

Sylvain CarleAssocié @ RealVentures

Le Web à QuébecAvril 2017

Pire. Sujet. Ever.

Tellement pas original(pour tout le monde)

Sylvain Carle

Le Web à QuébecAvril 2017

• intelligence artificielle: l'immense potentiel de l'apprentissage profond• deep learning avec tensorflow• apprendre l'apprentissage automatique• marketing et intelligence artificielle : état des lieux et regard sur le futur• la marque est morte. vive l’intelligente artificielle.• table ronde 1: agents conversationnels et conception d'intelligence artificielle• l’intelligence artificielle, opportunité ou menace?• intelligence artificielle et big data pour pmes: une approche simple par l’exemple• what ux design can teach ai• design & intelligence artificielle

IA: pourquoi et comment

Naviguer l’émergence de l’intelligence artificielle

Sylvain CarleAssocié @ RealVentures

Le Web à QuébecAvril 2017

https://twitter.com/sylvain

Je suis @sylvain

Je viens de l’internet. Geek assumé.

Nul n’est prophète en son pays, le retour de l’enfant prodigue, pis toute.

Un jour ou j’avais encore trop “perdu de temps” sur internet, je me suis demandé si ça pouvait devenir ça ma job. C’est ça.

Investir dans les startups qui misent sur l’intelligence artificielle pour changer la donne.

https://realventures.com

“AI First” c’est le nouveau “Mobile First”.

The Internet is providing the fabric on which the Information age is built and provides the network infrastructure to turn software into massively scalable platforms, whether centralized or decentralized (blockchain) enabling new services, products, experiences and business models;

Mobility is enabling 3 billion people to interact with their information and with each other anywhere at anytime and;

Connectivity added to other devices such as TVs, watches, cars, drones, clothes, robots, homes, AR/VR head mounted displays offers new ways to interact with and experience the world’s information; Continued improvements in Computing efficiency for different types of workloads whether in the cloud or at the edge is enabling new platforms and applications to emerge such as robotics, VR/AR, AI, blockchain, smart devices, etc.

The storage, usage and display (UI) of Big Data we and our businesses generate enable faster, more impactful, data driven decisions.

The digital revolution is now disrupting all aspects of society including retail, transportation, education, healthcare, financial services, work, real estate, energy, government, human communication, manufacturing, genomics, nanotechnology, etc.

La (r)évolution numérique touche l’ensemble des secteurs

The Internet is providing the fabric on which the Information age is built and provides the network infrastructure to turn software into massively scalable platforms, whether centralized or decentralized (blockchain) enabling new services, products, experiences and business models;

Mobility is enabling 3 billion people to interact with their information and with each other anywhere at anytime and;

Connectivity added to other devices such as TVs, watches, cars, drones, clothes, robots, homes, AR/VR head mounted displays offers new ways to interact with and experience the world’s information; Continued improvements in Computing efficiency for different types of workloads whether in the cloud or at the edge is enabling new platforms and applications to emerge such as robotics, VR/AR, AI, blockchain, smart devices, etc.

The storage, usage and display (UI) of Big Data we and our businesses generate enable faster, more impactful, data driven decisions.

The digital revolution is now disrupting all aspects of society including retail, transportation, education, healthcare, financial services, work, real estate, energy, government, human communication, manufacturing, genomics, nanotechnology, etc.

La (r)évolution numérique touche l’ensemble des secteurs

A I

DATA U I

CONNECT IV I T YMOB I L E

INTERNET

SOFTWARE

2008 20 1 1 2 0 1 4 20 1 7

L’évolution du stack

Google acquired Deepmind for $650M, launched Alphago, GPU cloud, AI datacenter optimization, tensorflow, imagecloud, translate, self driving cars, Health, and training all engineers in AI. Notable quotes: Sundai Pichai - “we will evolve in computing from a mobile-first to an AI-first world”; Eric Schmidt – “Machine learning will cause every successful IPO win in 5 years”, donated $4.5M to MILA in MTL for AI research;

Facebook open sourced AI framework, opened Messenger to bots, acquired Wit.ai, newsfeed powered by AI, launched Facebook AI Research (FAIR) lead by Yann Lecun, vision to build an AI agent per user;

Amazon: launched Echo (home voice platform), Alexa (intelligent personal assistant), AI Cloud, acquired Evi for $26M in 2012), acquired Angel.ai for chatbots, working on drones;

Microsoft acquired Maluuba, launched GPU cloud, Cortana, Microsoft AI Research, smart bot framework, invested in Element AI and donated $7M to MILA in MTL for AI research;

Apple acquired Siri for $400M, recruited Ruslan Salakhutdinov from Carnegie Mellon (ex-UofT) to lead AI research team, started open sourcing AI research, publicly stated importance of AI to success;

Others worth mentioning: Tesla, Uber (Acquired Otto for $680M, Geometric Intelligence) and leading car manufacturers with self-driving cars (GM bought Cruise for $1B), Nvidia and Intel (Nervana for $250M) for AI semiconductors, Salesforce (Metamind and Tempo AI acquisitions, Einstein initiative), Baidu, Tencent, Alibaba, Samsung (Viv), Netflix, IBM (Watson), Twitter (Magic Pony), GE (Bit Stew Systems);

Every successful company will require applied AI research to win and the market leaders are showing the way, acquiring 140 AI companies since 2011 including 40 in 2016 and hiring all the AI talent they can find…

Les leaders de l’industrie ne peuvent pas tout faire.

Il existe des tonnes d’opportunités pour les startups avec un fort bassin de talent pour développer ces produits et expériences en “mode IA”.

Soit vous rejoignez la marine royale, la garde côtière, ou alors vous êtes de pirates.

https://pirates.quebec/

https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/Pirate_Flag_of_Jack_Rackham.svg/1280px-Pirate_Flag_of_Jack_Rackham.svg.png

http://www.pinterest.com/pin/125608277076985104/

Not for redistribution 15

Element AI as co-founded by Yoshua Bengio, (founding father of AI renaissance) JF Gagné (co-founder of Planora, Chief Product officer at JDA Software), Nicolas Chapados (PhD, co-founded Apstat with Yoshua, Planora and hedge fund) and Real Ventures to build the world’s leading applied AI research lab and implementation platform to launch AI first solutions in partnership with large corporations and innovative startups;

Element helps companies make money with AI from fundamental research, to applied research, to application/solution development to implementation, monetization and/or joint ventures;

Element’s research lab is uniquely connected to the world best academic ecosystems including MILA, McGill, Poly, UofT, UBC, Microsoft and Cortana Research through its fellowship program and partnership with Microsoft;

Element completed initial round of founding co-led by Microsoft Ventures and Real Ventures in October ’16;

Element provides Real Ventures with credibility in the AI space, access to its network in AI academia and corporations, due diligence support, proprietary deal flow and applied AI research support for portfolio companies;

Real Ventures and Element AI are collaborating to build the AI ecosystem in Canada with projects including an AI Cloud, centers of excellence and world class events;

We are recognized leaders of the Canadian AI ecosystem as a result of co-founding applied AI research leader Element AI

http://www.pinterest.com/pin/125608277076955168/

1. La capacité d’accumulation (collecte, entreposage)

2. La rétrospective (voir, comprendre ce qui s’est passé)

3. L’analyse des signaux en temps réel (aggrégation et alertes)

4. Pouvoir émettre des recommendations (données et actions passées)

5. Capacité de prédictions (avec haut degré de certitude)

6. Prescription et automatisation (pur numérique et instrumentation)

Le modèle de maturité des données massives (big data)

Inspiré de https://en.wikipedia.org/wiki/Capability_Maturity_Model

1. La capacité d’accumulation (collecte, entreposage)

2. La rétrospective (voir, comprendre ce qui s’est passé)

3. L’analyse des signaux en temps réel (aggrégation et alertes)

4. Pouvoir émettre des recommendations (données et actions passées)

5. Capacité de prédictions (avec haut degré de certitude)

6. Prescription et automatisation (pur numérique et instrumentation)

Domaines d’applications émergentes de l’intelligence artificielle

Le modèle de maturité des données massives (big data)

1. IA générale (AGI) vs IA appliquée (narrow, specialized)

2. La définition de l’IA change tout le temps…

“John McCarthy, who invented the name Artificial Intelligence, noted, the definition of specialized AI is changing all of the time. Specifically, once a task formerly thought to characterize artificial intelligence becomes routine — like the aforementioned chess-playing, or Go, or a myriad of other taken-for-granted computer abilities — we no longer call it artificial intelligence.”

3. IA comme “intelligence augmentée”

Le contexte actuel pour de l’intelligence artificielle

http://cacm.acm.org/magazines/2012/1/144824-artificial-intelligence-past-and-future/fulltext https://stratechery.com/2017/the-arrival-of-artificial-intelligence/

Les nouvelles capacités par l’intelligence artificielle

Le point de bascule: quand la machine dépasse l’humain moyen dans la reconnaissance et l’interprétation des signaux dit “intelligents”. Le language, le monde qui nous entoure (prolifération de senseurs).

1. La lecture de textes (NLP sémantique).

2. La compréhension de la voix (NLP audio).

3. La reconnaissance visuelle (CV, classification).

4. Niveaux multiples d’interprétation et d’abstraction.

5. Apprentissage profonds, non-supervisé, par contre-exemples.

6. Mise en réseau et en comm

Les attributs qui propulsent l’intelligence artificielle

La “tempête parfaite”.

1. Publication ouverte. https://arXiv.org 2. Un graphe des chercheurs facile d’accès. https://scholar.google.ca/scholar?q=deep+learning+paper 3. La plupart de librairies de code en logiciel libre. 4. Beaucoup de cours gratuits en ligne. 5. Une communauté accueillante (l’esprit pédagogue de Y. Bengio) 6. Instituts de recherches “plusse meilleurs” : CIFAR, MILA, IVADO. 7. Accès aux données de références et nouvelles données

Les ressources pour aller plus vite

1. Tous les éléments mentionnés il y a 2 minutes… 2. Les APIs de grands joueurs (IBM Watson, Amazon, Google, Microsoft, Nuance et tant d’autres). 3. Les frameworks et API de startups (MLDB, Fuzzy.AI, plot.ly, Automat) 4. Les meetups de AI, ML, DL de Montréal (et Québec?) 5. Les grappes et super-grappes IA du Québec et du Canada 6. L’accélération des accélérateurs, la mutualisation des expertises 7. La nouvelle “Alliance IA du Québec” (dès demain, non?) 8. Vos idées?

1. L’impact économique et social de l’intelligence artificielle de la numérisation, de l’augmentation et de l’automatisation.

2. Les questions éthiques et légales soulevées par l’IA.

3. Les rôles et responsabilités des organisations publiques en IA, vs 1 et 2.

4. Le soutien à l’éducation, à la recherche et la mise en application de l’IA.

5. Le développement de nouvelles institutions et pratiques locales et internationales de gouvernance.

6. Le plan IA Québec?

Les défis devant nous cette année et pour 5, 10, 25 ans

Quelques liens (du bon stock)

1. Building an AI Startup: Realities & Tactics. http://mattturck.com/2016/09/29/building-an-ai-startup/ 2. O’Reilly Artificial Intelligence Newsletter. http://www.oreilly.com/ai/newsletter.html

3. Highlights from the O'Reilly AI Conference in New York 2016. https://www.oreilly.com/ideas/keynotes-from-ai-new-york-2016

4. The Competitive Landscape for Machine Intelligence (2016). https://hbr.org/2016/11/the-competitive-landscape-for-machine-intelligence et https://www.oreilly.com/ideas/the-current-state-of-machine-intelligence-3-0

5. The US Administration’s Report on the Future of Artificial Intelligence. https://obamawhitehouse.archives.gov/blog/2016/10/12/administrations-report-future-artificial-intelligence et Stratégie France IA - http://www.enseignementsup-recherche.gouv.fr/cid114739/rapport-strategie-france-i.a.-pour-le-developpement-des-technologies-d-intelligence-artificielle.html

6. 5 Big Predictions for Artificial Intelligence in 2017. https://www.technologyreview.com/s/603216/5-big-predictions-for-artificial-intelligence-in-2017/

7. A Sneak Peek at the State of AI 2016. https://medium.com/swlh/a-sneak-peek-at-the-state-of-ai-2016-d5d079e0c4de

8. Big Data et Intelligence Artificielle (panel IVADO des startups IA de Montréal). https://www.youtube.com/watch?v=g66X1lQpYZk&feature=youtu.be#t=72m49s

9. https://twitter.com/search?q=%22machine%20learning%22%20OR%20%22deep%20learning%22%20OR%20%22artificial%20intelligence%22%20OR%20AI%20OR%20ML%20OR%20DL%20OR%20%23AI%20from%3Afroginthevalley%20since%3A2015-01-03%20until%3A2017-04-05&src=typd

IA: pourquoi et comment

Merci!

Sylvain Carle

@sylvainsylvain@realventures.com

Le Web à QuébecAvril 2017