SlideShare uma empresa Scribd logo

Machine Learning: An Evolutionary Revolution

•Transferir como PPTX, PDF•
•
Pankaj Tirpude
Pankaj Tirpude

Here we will see, how Machine Learning has evolved throughout the history and what is the scope in future for it.

Tecnologia
1 de 34
(r)Evolution of Machine Learning FULLMETAL ALCHEMISTS
Machine Learning “Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. Machines will solve the kinds of problems now reserved for humans, and improve themselves.” - Dartmouth Summer Research Project on A.I., 1956.
What is Machine Learning? • Machines that learn and adapt to their environments Similar to living organisms Multimodal is goal AGI - endgame • New software/algorithms Neural networks Deep learning • New hardware GPU’s Neuromorphic chips • Cloud Enabled Intelligence in the cloud MLaaS, IaaS (Watson) Cloud Robotics
Machine Learning Methods • Supervised Learning • Unsupervised Learning • Semi-supervised Learning • Reinforcement Learning
Who’s using it? • Financial Services • Government • Healthcare • Marketing & Sales • Transportation • Oil & Gas
The Bigger Picture Universe Computer Science AI Machine Learning
Back In Time I (1760 - 1940) • 1760-1840 - Machine and labor Revolution • 1840-1920 - Technical Revolution • 1920 and further - Technological Revolution Medium of Communication developed like radio, television , telephone Informatics and the medium of transportation. Aviation and space exploration received a big push. Digital Revolution and Information Revolution.
Back In Time II (1940 - 1960) • 1940’s - First computers • 1950 - Turing Machine - Turing, A.M., Computing Machinery and Intelligence, Mind 49: 433-460, 1950 • 1951 - Minsky builds SNARC, a neural network at MIT • 1956 - Dartmouth Summer Research Project on A.I. • 1957 - Samuel drafts algos (Prinz) • 1959 - John McCarthy and Marvin Minsky founded the MIT AI Lab. • 1960’s - Ray Solomonoff lays the foundations of a mathematical theory of AI, introducing universal Bayesian methods for inductive inference and prediction.
Back In Time III (1960 - 1980) • 1967 - “nearest neighbour” algorithm, allowing computers to begin using very basic pattern recognition. • 1969 - Shakey the robot at Stanford • 1970’s - AI Winter I • 1970’s - Natural Language Processing (Symbolic) • 1979 - Music programmes by Kurzweil and Lucas • 1980 - First AAAI conference - Cultural transformation of Ford Motor company • 1980’s - Rule Based Expert Systems (Symbolic)
Back In Time IV (1980 - 2000) • 1981 - Connection Machine (parallel AI) • 1981 - Concept of Explanation based learning computer analyses training data and creates a general rule it can follow by discarding unimportant data. • 1985 - Back propagation • 1987 - “The Society of Mind” by Marvin Minsky published • 1990’s - AI Winter II (Narrow AI) • 1990’s - Automated Tropical Cyclone Forecasting System (ATCF) • 1994 - First self-driving car road test – in Paris • 1997 - Deep Blue beats Gary Kasparov
Back In Time V (2000 - ) • 2004 - DARPA introduces the DARPA Grand Challenge requiring competitors to produce autonomous vehicles for prize money. • 2007 - Checkers is solved by a team of researchers at the University of Alberta • 2009 - Google builds self driving car • 2010s - Statistical Machine Learning, algorithms that learn from raw data • 2011 – IBM’s Watson beats Ken Jennings and Brad Rutter on Jeopardy • 2012 - Deep Learning (Sub-Symbolic) • 2013 - E.U. Human Brain Project (model brain by 2023) • 2014 - Human vision surpassed by ML systems at Google, Baidu, Facebook • 2015 - Machine dreaming (Google and Facebook NN’s)
Timeline
ML Applications 1.0 • Finance  Asset allocation  Algo-trading • Fraud detection • Cybersecurity • E-Commerce • Search • Manufacturing • Medicine • Law • Business Analytics • Ad serving • Recommendation engines • Robotics  Industry  Consumer  Space  Military • UAV (cars, drones etc.) • Scientific discovery • Mathematical theorems • Route Planning • Virtual Assistants • Personalization • Compose music • Write stories • Smart homes
ML Applications 2.0 • Computer vision • Speech recognition • NLP • Translation • Call centers • Rescue operations • Policing • Military • Political • National security • Anything a human can do but faster and more accurate – creating, reasoning, decision making, prediction • Google – introduced 50 ML products in last 2 years (Jeff Dean)
ML Applications – Examples 1.0 • The heavily hyped, self-driving Google car? The essence of machine learning. • Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. • Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. • Fraud detection? One of the more obvious, important uses in our world today.
ML Applications – Examples 2.0 • AI can do all these things already today: Translating an article from Chinese to English Translating speech from Chinese to English, in real time Identifying all the chairs/faces in an image Transcribing a conversation at a party (with background noise) Folding your laundry (robotics) Proving new theorems (ATP) Automatically replying to your email, and scheduling
Learning and doing from watching videos • Researchers at the University of Maryland, funded by DARPA’s Mathematics of Sensing, Exploitation and Execution (MSEE) program. • System that enables robots to process visual data from a series of “how to” cooking videos on YouTube - and then cook a meal.
ML Performance evaluation • Optimal: it is not possible to perform better Checkers, Rubik’s cube, some poker • Strong super-human: performs better than all humans Chess, scrabble, question-answer • Super-human: performs better than most humans Backgammon, cars, crosswords • Par-human: performs similarly to most humans Go, Image recognition, OCR • Sub-human: performs worse than most humans Translation, speech recognition, handwriting
ML Companies - MNC • IBM Watson • Google Deepmind etc. • Microsoft Project Adam • Facebook • Baidu • Yahoo!
ML Companies - startups • Numenta • OpenCog • Vicarious • Clarafai • Sentient • Nurture • Wit.ai • Cortical.io • Viv.ai Number is growing rapidly (daily?)
ML “Rockstars” • Andrew Ng (Baidu) • Geoff Hinton (Google) • Yann LeCun (Facebook) • Yoshua Bengio (IBM) • Michael Jordan • Jurgen Schmidhuber • Marcus Hutter
Some (Famous) ML Research Groups • Godel Machine (IDSIA) • AIXI (IDSIA/ANU) • CSAIL (MIT) • AmpLab (Berkeley) • Stanford • CMU • NYU • CBL Lab (Cambridge) • Oxford • Imperial College • UCL Gatsby Lab • Toronto • DARPA (funding)
Movies that used ML concepts • I, Robot • Bicentennial Man • A Beautiful Mind • The Matrix Trilogy • 21 • The Imitation Game • Artificial Intelligence • Her • Blade Runner • Ex Machina • Money-ball • Terminator Series
Robotics - Embodied ML 1. Industrial Robotics • Manufacturing (Baxter) • Warehousing (Amazon) • Police/Security • Military • Surgery • Drones (UAV’s) Self-driving cars Trains Ships Planes Underwater
Robotics – Embodied ML 2. Consumer Robotics • Robots with friendly user interface that can understand user’s emotions Visual; facial emotions Tone of voice • Caretaking • EmoSpark, Echo • Education • Home security • Housekeeping • Companionship • Artificial limbs • Exoskeletons
Robots & Robotics Companies • Sawyer (ReThink) • iCub (EU) • Asimo (Honda) • Nao (Aldebaran) • Pepper (Softbank) • Many (Google) • Roomba (iRobot) • Kiva (Amazon) • Many (KUKA) • Jibo (startup) • Milo (Robokind) • Oshbot (Fellows) • Valkyrie (NASA)
Opportunities • Free humans to pursue arts and sciences The Venus Project • Solve deep challenges (political, economic, scientific, social) • Accelerate new discoveries in science, technology, medicine (illness and aging) • Creation of new types of jobs • Increased efficiencies in every market space Industry 4.0 (steam, electric, digital, intelligence) • Faster, cheaper, more accurate • Replace mundane, repetitive jobs • Human-Robot collaboration • A smarter planet
Threats • Unemployment due to automation Replace some jobs but create new ones? What will these be? • Widen the inequality gap New economic paradigm needed Basic Income Guarantee Existential risk AI Safety FHI/FLI/CSER/MIRI • Legal + Ethical issues New laws Machine rights Personhood
Predictions??? • More robots (exponential increase) • More automation (everywhere)  Endgame is to automate all work  50% will be automated by 2035 • Loosely autonomous agents (2015) • Semi-autonomous agents (2020) • Fully autonomous agents (2025) • Cyborgs (has started – biohackers, implants) • Singularity (2029?) – smarter than us • Self-aware? (personhood) • Quantum computing  Game changer  Quantum algorithms  D-wave  Advances in science and medicine • Ethics (more debate) • Regulation (safety issues)
Rise of the Robots
References I • Rise of the Machines – The Economist, May 9th, 2015 http://www.economist.com/news/briefing/21650526-artificial-intelligence-scares- peopleexcessively-so-rise-machines • Microsoft Challenges Google’s Artificial Brain with “Project Adam” http://www.wired.com/2014/07/microsoft-adam/ • The Future of Artificial Intelligence According to Ben Goertzel http://techemergence.com/the-future-of-artificial-intelligence-according-to-Ben- goertzel/ • Kurzweil: Human-Level AI Is Coming By 2029 http://uk.businessinsider.com/ray-kurzweil-thinks-well-have-human-level-ai-by-2029- 2014-12?r=US • Zuckerberg and Musk back software startup that mimics human learning http://www.theguardian.com/technology/2014/mar/21/zuckerberg-invest-startup- brain-software-vicarious • Computer with human-like learning will program itself http://www.newscientist.com/article/mg22429932.200-computer-with-humanlike- learning-will-program-itself.html#.VLQccHs5XUs • Google’s Grand Plan to Make Your Brain Irrelevant http://www.wired.com/2014/01/google-buying-way-making-brain-irrelevant/
References II • The Race to Buy the Human Brains Behind Deep Learning Machines http://www.businessweek.com/articles/2014-01-27/the-race-to-buy-the-human- brains-behind-deep-learning-machines • Smarter algorithms will power our future digital lives http://www.computerworld.com/article/2687902/smarter-algorithms-will-power- our-future-digital-lives.html • What We Know About Deep Learning Is Just The Tip Of The Iceberg https://wtvox.com/2014/12/know-deep-learning-just-tip-iceberg/ • 10 Signs You Should Invest In Artificial Intelligence http://www.33rdsquare.com/2014/10/10-signs-you-should-invest-in.html • Towards Intelligent Humanoid Robots http://www.33rdsquare.com/2013/02/towards-intelligent-humanoid-robots.html • The Deep Mind of Demis Hassabis https://medium.com/backchannel/the-deep-mind-of-demis-hassabis- 156112890d8a4a • Google isn’t the only company working on artificial intelligence, it’s just the richest https://gigaom.com/2014/01/29/google-isnt-the-only-company-working-on- artificial-intelligence-its-just-the-richest/
Bibliography • Barrat, James, Our Final Invention, St. Martin's Griffin, 2014 • Bengio, Yoshua et al, Deep Learning, MIT Press, 2015 • Brynjolfsson, Erik and Andrew McAfee, The Second Machine Age, W.W. Norton & Co., 2014 • Byrne, Fergal, Real Machine Intelligence, Leanpub, 2015 • Ford, Martin, Rise of the Robots: Technology and the Threat of a Jobless Future, Basic Books, 2015 • Kaku, Michio, The Future of the Mind, Doubleday, 2014 • Kurzweil, Ray, The Singularity is Near, Penguin Books, 2006 • Kurzweil, Ray, How to Create a Mind, Penguin Books, 2013 • Nowak, Peter, Humans 3.0: The Upgrading of the Species, Lyons Press, 2015 • Russell and Norvig, Artificial Intelligence, A Modern Approach, Pearson, 2009 • Yampolskiy, Roman - Artificial Superintelligence, A Futuristic Approach, CRC, 2015
Questions “A company that cracks human level intelligence will be worth ten Microsofts” – Bill Gates.

Recomendados

A brief history of machine learning
A brief history of machine learningA brief history of machine learning
A brief history of machine learningRobert Colner
 
Introduction to AI & ML
Introduction to AI & MLIntroduction to AI & ML
Introduction to AI & MLMandy Sidana
 
Machine Learning
Machine LearningMachine Learning
Machine LearningDarshan Ambhaikar
 
Machine Learning
Machine LearningMachine Learning
Machine LearningRahul Kumar
 
Machine learning seminar presentation
Machine learning seminar presentationMachine learning seminar presentation
Machine learning seminar presentationsweety seth
 
Lecture #1: Introduction to machine learning (ML)
Lecture #1: Introduction to machine learning (ML)Lecture #1: Introduction to machine learning (ML)
Lecture #1: Introduction to machine learning (ML)butest
 
Machine learning
Machine learningMachine learning
Machine learningRajib Kumar De
 
Machine learning seminar ppt
Machine learning seminar pptMachine learning seminar ppt
Machine learning seminar pptRAHUL DANGWAL
 

Recomendados

A brief history of machine learning
A brief history of machine learningA brief history of machine learning
A brief history of machine learningRobert Colner
 
Introduction to AI & ML
Introduction to AI & MLIntroduction to AI & ML
Introduction to AI & MLMandy Sidana
 
Machine Learning
Machine LearningMachine Learning
Machine LearningDarshan Ambhaikar
 
Machine Learning
Machine LearningMachine Learning
Machine LearningRahul Kumar
 
Machine learning seminar presentation
Machine learning seminar presentationMachine learning seminar presentation
Machine learning seminar presentationsweety seth
 
Lecture #1: Introduction to machine learning (ML)
Lecture #1: Introduction to machine learning (ML)Lecture #1: Introduction to machine learning (ML)
Lecture #1: Introduction to machine learning (ML)butest
 
Machine learning
Machine learningMachine learning
Machine learningRajib Kumar De
 
Machine learning seminar ppt
Machine learning seminar pptMachine learning seminar ppt
Machine learning seminar pptRAHUL DANGWAL
 
Deep neural networks
Deep neural networksDeep neural networks
Deep neural networksSi Haem
 
Support Vector Machines
Support Vector MachinesSupport Vector Machines
Support Vector Machinesnextlib
 
Deep learning seminar report
Deep learning seminar reportDeep learning seminar report
Deep learning seminar reportSKS
 
Machine Learning ppt
Machine Learning pptMachine Learning ppt
Machine Learning pptStudent Conscious Club
 
Deep learning
Deep learningDeep learning
Deep learningMohamed Loey
 
Module 4: Model Selection and Evaluation
Module 4: Model Selection and EvaluationModule 4: Model Selection and Evaluation
Module 4: Model Selection and EvaluationSara Hooker
 
Machine learning
Machine learningMachine learning
Machine learningRajesh Chittampally
 
ppt
pptppt
pptbutest
 
Machine learning
Machine learningMachine learning
Machine learningDr Geetha Mohan
 
Artificial intelligence of things(AIoT): What is AIoT: AIoT applications
Artificial intelligence of things(AIoT): What is AIoT: AIoT applicationsArtificial intelligence of things(AIoT): What is AIoT: AIoT applications
Artificial intelligence of things(AIoT): What is AIoT: AIoT applicationsAnusha Aravindan
 
Deep learning - A Visual Introduction
Deep learning - A Visual IntroductionDeep learning - A Visual Introduction
Deep learning - A Visual IntroductionLukas Masuch
 
Machine Learning Using Python
Machine Learning Using PythonMachine Learning Using Python
Machine Learning Using PythonSavitaHanchinal
 
Lecture 1: What is Machine Learning?
Lecture 1: What is Machine Learning?Lecture 1: What is Machine Learning?
Lecture 1: What is Machine Learning?Marina Santini
 
Types of Machine Learning
Types of Machine LearningTypes of Machine Learning
Types of Machine LearningSamra Shahzadi
 
Supervised Unsupervised and Reinforcement Learning
Supervised Unsupervised and Reinforcement Learning Supervised Unsupervised and Reinforcement Learning
Supervised Unsupervised and Reinforcement Learning Aakash Chotrani
 
Robotics and ai
Robotics and ai Robotics and ai
Robotics and ai AnilPokhrel7
 
What is Deep Learning?
What is Deep Learning?What is Deep Learning?
What is Deep Learning?NVIDIA
 
Neural networks and deep learning
Neural networks and deep learningNeural networks and deep learning
Neural networks and deep learningJĂśrgen Sandig
 
Recurrent Neural Networks, LSTM and GRU
Recurrent Neural Networks, LSTM and GRURecurrent Neural Networks, LSTM and GRU
Recurrent Neural Networks, LSTM and GRUananth
 
Introduction to Deep Learning
Introduction to Deep Learning Introduction to Deep Learning
Introduction to Deep Learning Salesforce Engineering
 
The Future of Machine Learning
The Future of Machine LearningThe Future of Machine Learning
The Future of Machine LearningRussell Miles
 
Machine Learning - Where to Next?, May 2015
Machine Learning - Where to Next?, May 2015Machine Learning - Where to Next?, May 2015
Machine Learning - Where to Next?, May 2015Peter Morgan
 

Mais conteĂşdo relacionado

Mais procurados

Deep neural networks
Deep neural networksDeep neural networks
Deep neural networksSi Haem
 
Support Vector Machines
Support Vector MachinesSupport Vector Machines
Support Vector Machinesnextlib
 
Deep learning seminar report
Deep learning seminar reportDeep learning seminar report
Deep learning seminar reportSKS
 
Deep learning
Deep learningDeep learning
Deep learningMohamed Loey
 
Module 4: Model Selection and Evaluation
Module 4: Model Selection and EvaluationModule 4: Model Selection and Evaluation
Module 4: Model Selection and EvaluationSara Hooker
 
ppt
pptppt
pptbutest
 
Artificial intelligence of things(AIoT): What is AIoT: AIoT applications
Artificial intelligence of things(AIoT): What is AIoT: AIoT applicationsArtificial intelligence of things(AIoT): What is AIoT: AIoT applications
Artificial intelligence of things(AIoT): What is AIoT: AIoT applicationsAnusha Aravindan
 
Deep learning - A Visual Introduction
Deep learning - A Visual IntroductionDeep learning - A Visual Introduction
Deep learning - A Visual IntroductionLukas Masuch
 
Machine Learning Using Python
Machine Learning Using PythonMachine Learning Using Python
Machine Learning Using PythonSavitaHanchinal
 
Lecture 1: What is Machine Learning?
Lecture 1: What is Machine Learning?Lecture 1: What is Machine Learning?
Lecture 1: What is Machine Learning?Marina Santini
 
Types of Machine Learning
Types of Machine LearningTypes of Machine Learning
Types of Machine LearningSamra Shahzadi
 
Supervised Unsupervised and Reinforcement Learning
Supervised Unsupervised and Reinforcement Learning Supervised Unsupervised and Reinforcement Learning
Supervised Unsupervised and Reinforcement Learning Aakash Chotrani
 
Robotics and ai
Robotics and ai Robotics and ai
Robotics and ai AnilPokhrel7
 
What is Deep Learning?
What is Deep Learning?What is Deep Learning?
What is Deep Learning?NVIDIA
 
Neural networks and deep learning
Neural networks and deep learningNeural networks and deep learning
Neural networks and deep learningJĂśrgen Sandig
 
Recurrent Neural Networks, LSTM and GRU
Recurrent Neural Networks, LSTM and GRURecurrent Neural Networks, LSTM and GRU
Recurrent Neural Networks, LSTM and GRUananth
 

Mais procurados (20)

Deep neural networks
Deep neural networksDeep neural networks
Deep neural networks
 
Support Vector Machines
Support Vector MachinesSupport Vector Machines
Support Vector Machines
 
Deep learning seminar report
Deep learning seminar reportDeep learning seminar report
Deep learning seminar report
 
Machine Learning ppt
Machine Learning pptMachine Learning ppt
Machine Learning ppt
 
Deep learning
Deep learningDeep learning
Deep learning
 
Module 4: Model Selection and Evaluation
Module 4: Model Selection and EvaluationModule 4: Model Selection and Evaluation
Module 4: Model Selection and Evaluation
 
Machine learning
Machine learningMachine learning
Machine learning
 
ppt
pptppt
ppt
 
Machine learning
Machine learningMachine learning
Machine learning
 
Artificial intelligence of things(AIoT): What is AIoT: AIoT applications
Artificial intelligence of things(AIoT): What is AIoT: AIoT applicationsArtificial intelligence of things(AIoT): What is AIoT: AIoT applications
Artificial intelligence of things(AIoT): What is AIoT: AIoT applications
 
Deep learning - A Visual Introduction
Deep learning - A Visual IntroductionDeep learning - A Visual Introduction
Deep learning - A Visual Introduction
 
Machine Learning Using Python
Machine Learning Using PythonMachine Learning Using Python
Machine Learning Using Python
 
Lecture 1: What is Machine Learning?
Lecture 1: What is Machine Learning?Lecture 1: What is Machine Learning?
Lecture 1: What is Machine Learning?
 
Types of Machine Learning
Types of Machine LearningTypes of Machine Learning
Types of Machine Learning
 
Supervised Unsupervised and Reinforcement Learning
Supervised Unsupervised and Reinforcement Learning Supervised Unsupervised and Reinforcement Learning
Supervised Unsupervised and Reinforcement Learning
 
Robotics and ai
Robotics and ai Robotics and ai
Robotics and ai
 
What is Deep Learning?
What is Deep Learning?What is Deep Learning?
What is Deep Learning?
 
Neural networks and deep learning
Neural networks and deep learningNeural networks and deep learning
Neural networks and deep learning
 
Recurrent Neural Networks, LSTM and GRU
Recurrent Neural Networks, LSTM and GRURecurrent Neural Networks, LSTM and GRU
Recurrent Neural Networks, LSTM and GRU
 
Introduction to Deep Learning
Introduction to Deep Learning Introduction to Deep Learning
Introduction to Deep Learning
 

Semelhante a Machine Learning: An Evolutionary Revolution

The Future of Machine Learning
The Future of Machine LearningThe Future of Machine Learning
The Future of Machine LearningRussell Miles
 
Machine Learning - Where to Next?, May 2015
Machine Learning - Where to Next?, May 2015Machine Learning - Where to Next?, May 2015
Machine Learning - Where to Next?, May 2015Peter Morgan
 
AI and Healthcare 2023.pdf
AI and Healthcare 2023.pdfAI and Healthcare 2023.pdf
AI and Healthcare 2023.pdfKR_Barker
 
AI and Healthcare 2023.pdf
AI and Healthcare 2023.pdfAI and Healthcare 2023.pdf
AI and Healthcare 2023.pdfKR_Barker
 
AI and Healthcare: An Overview (January 2024)
AI and Healthcare: An Overview (January 2024)AI and Healthcare: An Overview (January 2024)
AI and Healthcare: An Overview (January 2024)KR_Barker
 
The Human Dimension of Industry 4.0
The Human Dimension of Industry 4.0The Human Dimension of Industry 4.0
The Human Dimension of Industry 4.0Andy Fawkes
 
An introduction to Deep Learning
An introduction to Deep LearningAn introduction to Deep Learning
An introduction to Deep LearningAmazon Web Services
 
Artificial Intelligence in Civil Engineering
Artificial Intelligence in Civil EngineeringArtificial Intelligence in Civil Engineering
Artificial Intelligence in Civil EngineeringAnsari Usama
 
AI and Healthcare 2022.pdf
AI and Healthcare 2022.pdfAI and Healthcare 2022.pdf
AI and Healthcare 2022.pdfKR_Barker
 
Introduction to Deep Learning (September 2017)
Introduction to Deep Learning (September 2017)Introduction to Deep Learning (September 2017)
Introduction to Deep Learning (September 2017)Julien SIMON
 
AI and Healthcare- updated January 2019
AI and Healthcare- updated January 2019AI and Healthcare- updated January 2019
AI and Healthcare- updated January 2019KR_Barker
 
Artificial intelligence
Artificial intelligenceArtificial intelligence
Artificial intelligencedivinemathew358
 
Maritime Information Warfare - The Human Dimension
Maritime Information Warfare - The Human DimensionMaritime Information Warfare - The Human Dimension
Maritime Information Warfare - The Human DimensionAndy Fawkes
 
Robotics Overview 2016
Robotics Overview 2016Robotics Overview 2016
Robotics Overview 2016Peter Morgan
 
Robotics Overview 2016
Robotics Overview 2016Robotics Overview 2016
Robotics Overview 2016Peter Morgan
 
Artificial intelligence
Artificial intelligenceArtificial intelligence
Artificial intelligenceMargaret Cameron
 
"Taming the machine" - Wie regulieren wir disruptive Technologien?
"Taming the machine" - Wie regulieren wir disruptive Technologien?"Taming the machine" - Wie regulieren wir disruptive Technologien?
"Taming the machine" - Wie regulieren wir disruptive Technologien?Hans Bellstedt Public Affairs GmbH
 

Semelhante a Machine Learning: An Evolutionary Revolution (20)

The Future of Machine Learning
The Future of Machine LearningThe Future of Machine Learning
The Future of Machine Learning
 
Machine Learning - Where to Next?, May 2015
Machine Learning - Where to Next?, May 2015Machine Learning - Where to Next?, May 2015
Machine Learning - Where to Next?, May 2015
 
AI and Healthcare 2023.pdf
AI and Healthcare 2023.pdfAI and Healthcare 2023.pdf
AI and Healthcare 2023.pdf
 
AI and Healthcare 2023.pdf
AI and Healthcare 2023.pdfAI and Healthcare 2023.pdf
AI and Healthcare 2023.pdf
 
AI and Healthcare: An Overview (January 2024)
AI and Healthcare: An Overview (January 2024)AI and Healthcare: An Overview (January 2024)
AI and Healthcare: An Overview (January 2024)
 
The Human Dimension of Industry 4.0
The Human Dimension of Industry 4.0The Human Dimension of Industry 4.0
The Human Dimension of Industry 4.0
 
An introduction to Deep Learning
An introduction to Deep LearningAn introduction to Deep Learning
An introduction to Deep Learning
 
When AI becomes a data-driven machine, and digital is everywhere!
When AI becomes a data-driven machine, and digital is everywhere!When AI becomes a data-driven machine, and digital is everywhere!
When AI becomes a data-driven machine, and digital is everywhere!
 
Artificial Intelligence in Civil Engineering
Artificial Intelligence in Civil EngineeringArtificial Intelligence in Civil Engineering
Artificial Intelligence in Civil Engineering
 
AI and Healthcare 2022.pdf
AI and Healthcare 2022.pdfAI and Healthcare 2022.pdf
AI and Healthcare 2022.pdf
 
Introduction to Deep Learning (September 2017)
Introduction to Deep Learning (September 2017)Introduction to Deep Learning (September 2017)
Introduction to Deep Learning (September 2017)
 
AI and Healthcare- updated January 2019
AI and Healthcare- updated January 2019AI and Healthcare- updated January 2019
AI and Healthcare- updated January 2019
 
Kk_FPS.pptx
Kk_FPS.pptxKk_FPS.pptx
Kk_FPS.pptx
 
Artificial intelligence
Artificial intelligenceArtificial intelligence
Artificial intelligence
 
Maritime Information Warfare - The Human Dimension
Maritime Information Warfare - The Human DimensionMaritime Information Warfare - The Human Dimension
Maritime Information Warfare - The Human Dimension
 
Robotics Overview 2016
Robotics Overview 2016Robotics Overview 2016
Robotics Overview 2016
 
Robotics Overview 2016
Robotics Overview 2016Robotics Overview 2016
Robotics Overview 2016
 
Artificial intelligence
Artificial intelligenceArtificial intelligence
Artificial intelligence
 
Fps
FpsFps
Fps
 
"Taming the machine" - Wie regulieren wir disruptive Technologien?
"Taming the machine" - Wie regulieren wir disruptive Technologien?"Taming the machine" - Wie regulieren wir disruptive Technologien?
"Taming the machine" - Wie regulieren wir disruptive Technologien?
 

Último

The Evolution of Money: Digital Transformation and CBDCs in Central Banking
The Evolution of Money: Digital Transformation and CBDCs in Central BankingThe Evolution of Money: Digital Transformation and CBDCs in Central Banking
The Evolution of Money: Digital Transformation and CBDCs in Central BankingSelcen Ozturkcan
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitecturePixlogix Infotech
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Igalia
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityPrincipled Technologies
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsEnterprise Knowledge
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonAnna Loughnan Colquhoun
 
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | DelhiFULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhisoniya singh
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure servicePooja Nehwal
 
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘RTylerCroy
 
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for PartnersEnhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for PartnersThousandEyes
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 3652toLead Limited
 
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Paola De la Torre
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking MenDelhi Call girls
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024BookNet Canada
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel AraĂşjo
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking MenDelhi Call girls
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdfhans926745
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slidevu2urc
 

Último (20)

The Evolution of Money: Digital Transformation and CBDCs in Central Banking
The Evolution of Money: Digital Transformation and CBDCs in Central BankingThe Evolution of Money: Digital Transformation and CBDCs in Central Banking
The Evolution of Money: Digital Transformation and CBDCs in Central Banking
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC Architecture
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | DelhiFULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
FULL ENJOY 🔝 8264348440 🔝 Call Girls in Diplomatic Enclave | Delhi
 
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure serviceWhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
WhatsApp 9892124323 ✓Call Girls In Kalyan ( Mumbai ) secure service
 
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
 
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for PartnersEnhancing Worker Digital Experience: A Hands-on Workshop for Partners
Enhancing Worker Digital Experience: A Hands-on Workshop for Partners
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
 
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men08448380779 Call Girls In Friends Colony Women Seeking Men
08448380779 Call Girls In Friends Colony Women Seeking Men
 
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
#StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 

Machine Learning: An Evolutionary Revolution

  • 1. (r)Evolution of Machine Learning FULLMETAL ALCHEMISTS
  • 2. Machine Learning “Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. Machines will solve the kinds of problems now reserved for humans, and improve themselves.” - Dartmouth Summer Research Project on A.I., 1956.
  • 3. What is Machine Learning? • Machines that learn and adapt to their environments Similar to living organisms Multimodal is goal AGI - endgame • New software/algorithms Neural networks Deep learning • New hardware GPU’s Neuromorphic chips • Cloud Enabled Intelligence in the cloud MLaaS, IaaS (Watson) Cloud Robotics
  • 4. Machine Learning Methods • Supervised Learning • Unsupervised Learning • Semi-supervised Learning • Reinforcement Learning
  • 5. Who’s using it? • Financial Services • Government • Healthcare • Marketing & Sales • Transportation • Oil & Gas
  • 6. The Bigger Picture Universe Computer Science AI Machine Learning
  • 7. Back In Time I (1760 - 1940) • 1760-1840 - Machine and labor Revolution • 1840-1920 - Technical Revolution • 1920 and further - Technological Revolution Medium of Communication developed like radio, television , telephone Informatics and the medium of transportation. Aviation and space exploration received a big push. Digital Revolution and Information Revolution.
  • 8. Back In Time II (1940 - 1960) • 1940’s - First computers • 1950 - Turing Machine - Turing, A.M., Computing Machinery and Intelligence, Mind 49: 433-460, 1950 • 1951 - Minsky builds SNARC, a neural network at MIT • 1956 - Dartmouth Summer Research Project on A.I. • 1957 - Samuel drafts algos (Prinz) • 1959 - John McCarthy and Marvin Minsky founded the MIT AI Lab. • 1960’s - Ray Solomonoff lays the foundations of a mathematical theory of AI, introducing universal Bayesian methods for inductive inference and prediction.
  • 9. Back In Time III (1960 - 1980) • 1967 - “nearest neighbour” algorithm, allowing computers to begin using very basic pattern recognition. • 1969 - Shakey the robot at Stanford • 1970’s - AI Winter I • 1970’s - Natural Language Processing (Symbolic) • 1979 - Music programmes by Kurzweil and Lucas • 1980 - First AAAI conference - Cultural transformation of Ford Motor company • 1980’s - Rule Based Expert Systems (Symbolic)
  • 10. Back In Time IV (1980 - 2000) • 1981 - Connection Machine (parallel AI) • 1981 - Concept of Explanation based learning computer analyses training data and creates a general rule it can follow by discarding unimportant data. • 1985 - Back propagation • 1987 - “The Society of Mind” by Marvin Minsky published • 1990’s - AI Winter II (Narrow AI) • 1990’s - Automated Tropical Cyclone Forecasting System (ATCF) • 1994 - First self-driving car road test – in Paris • 1997 - Deep Blue beats Gary Kasparov
  • 11. Back In Time V (2000 - ) • 2004 - DARPA introduces the DARPA Grand Challenge requiring competitors to produce autonomous vehicles for prize money. • 2007 - Checkers is solved by a team of researchers at the University of Alberta • 2009 - Google builds self driving car • 2010s - Statistical Machine Learning, algorithms that learn from raw data • 2011 – IBM’s Watson beats Ken Jennings and Brad Rutter on Jeopardy • 2012 - Deep Learning (Sub-Symbolic) • 2013 - E.U. Human Brain Project (model brain by 2023) • 2014 - Human vision surpassed by ML systems at Google, Baidu, Facebook • 2015 - Machine dreaming (Google and Facebook NN’s)
  • 13. ML Applications 1.0 • Finance  Asset allocation  Algo-trading • Fraud detection • Cybersecurity • E-Commerce • Search • Manufacturing • Medicine • Law • Business Analytics • Ad serving • Recommendation engines • Robotics  Industry  Consumer  Space  Military • UAV (cars, drones etc.) • Scientific discovery • Mathematical theorems • Route Planning • Virtual Assistants • Personalization • Compose music • Write stories • Smart homes
  • 14. ML Applications 2.0 • Computer vision • Speech recognition • NLP • Translation • Call centers • Rescue operations • Policing • Military • Political • National security • Anything a human can do but faster and more accurate – creating, reasoning, decision making, prediction • Google – introduced 50 ML products in last 2 years (Jeff Dean)
  • 15. ML Applications – Examples 1.0 • The heavily hyped, self-driving Google car? The essence of machine learning. • Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. • Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. • Fraud detection? One of the more obvious, important uses in our world today.
  • 16. ML Applications – Examples 2.0 • AI can do all these things already today: Translating an article from Chinese to English Translating speech from Chinese to English, in real time Identifying all the chairs/faces in an image Transcribing a conversation at a party (with background noise) Folding your laundry (robotics) Proving new theorems (ATP) Automatically replying to your email, and scheduling
  • 17. Learning and doing from watching videos • Researchers at the University of Maryland, funded by DARPA’s Mathematics of Sensing, Exploitation and Execution (MSEE) program. • System that enables robots to process visual data from a series of “how to” cooking videos on YouTube - and then cook a meal.
  • 18. ML Performance evaluation • Optimal: it is not possible to perform better Checkers, Rubik’s cube, some poker • Strong super-human: performs better than all humans Chess, scrabble, question-answer • Super-human: performs better than most humans Backgammon, cars, crosswords • Par-human: performs similarly to most humans Go, Image recognition, OCR • Sub-human: performs worse than most humans Translation, speech recognition, handwriting
  • 19. ML Companies - MNC • IBM Watson • Google Deepmind etc. • Microsoft Project Adam • Facebook • Baidu • Yahoo!
  • 20. ML Companies - startups • Numenta • OpenCog • Vicarious • Clarafai • Sentient • Nurture • Wit.ai • Cortical.io • Viv.ai Number is growing rapidly (daily?)
  • 21. ML “Rockstars” • Andrew Ng (Baidu) • Geoff Hinton (Google) • Yann LeCun (Facebook) • Yoshua Bengio (IBM) • Michael Jordan • Jurgen Schmidhuber • Marcus Hutter
  • 22. Some (Famous) ML Research Groups • Godel Machine (IDSIA) • AIXI (IDSIA/ANU) • CSAIL (MIT) • AmpLab (Berkeley) • Stanford • CMU • NYU • CBL Lab (Cambridge) • Oxford • Imperial College • UCL Gatsby Lab • Toronto • DARPA (funding)
  • 23. Movies that used ML concepts • I, Robot • Bicentennial Man • A Beautiful Mind • The Matrix Trilogy • 21 • The Imitation Game • Artificial Intelligence • Her • Blade Runner • Ex Machina • Money-ball • Terminator Series
  • 24. Robotics - Embodied ML 1. Industrial Robotics • Manufacturing (Baxter) • Warehousing (Amazon) • Police/Security • Military • Surgery • Drones (UAV’s) Self-driving cars Trains Ships Planes Underwater
  • 25. Robotics – Embodied ML 2. Consumer Robotics • Robots with friendly user interface that can understand user’s emotions Visual; facial emotions Tone of voice • Caretaking • EmoSpark, Echo • Education • Home security • Housekeeping • Companionship • Artificial limbs • Exoskeletons
  • 26. Robots & Robotics Companies • Sawyer (ReThink) • iCub (EU) • Asimo (Honda) • Nao (Aldebaran) • Pepper (Softbank) • Many (Google) • Roomba (iRobot) • Kiva (Amazon) • Many (KUKA) • Jibo (startup) • Milo (Robokind) • Oshbot (Fellows) • Valkyrie (NASA)
  • 27. Opportunities • Free humans to pursue arts and sciences The Venus Project • Solve deep challenges (political, economic, scientific, social) • Accelerate new discoveries in science, technology, medicine (illness and aging) • Creation of new types of jobs • Increased efficiencies in every market space Industry 4.0 (steam, electric, digital, intelligence) • Faster, cheaper, more accurate • Replace mundane, repetitive jobs • Human-Robot collaboration • A smarter planet
  • 28. Threats • Unemployment due to automation Replace some jobs but create new ones? What will these be? • Widen the inequality gap New economic paradigm needed Basic Income Guarantee Existential risk AI Safety FHI/FLI/CSER/MIRI • Legal + Ethical issues New laws Machine rights Personhood
  • 29. Predictions??? • More robots (exponential increase) • More automation (everywhere)  Endgame is to automate all work  50% will be automated by 2035 • Loosely autonomous agents (2015) • Semi-autonomous agents (2020) • Fully autonomous agents (2025) • Cyborgs (has started – biohackers, implants) • Singularity (2029?) – smarter than us • Self-aware? (personhood) • Quantum computing  Game changer  Quantum algorithms  D-wave  Advances in science and medicine • Ethics (more debate) • Regulation (safety issues)
  • 30. Rise of the Robots
  • 31. References I • Rise of the Machines – The Economist, May 9th, 2015 http://www.economist.com/news/briefing/21650526-artificial-intelligence-scares- peopleexcessively-so-rise-machines • Microsoft Challenges Google’s Artificial Brain with “Project Adam” http://www.wired.com/2014/07/microsoft-adam/ • The Future of Artificial Intelligence According to Ben Goertzel http://techemergence.com/the-future-of-artificial-intelligence-according-to-Ben- goertzel/ • Kurzweil: Human-Level AI Is Coming By 2029 http://uk.businessinsider.com/ray-kurzweil-thinks-well-have-human-level-ai-by-2029- 2014-12?r=US • Zuckerberg and Musk back software startup that mimics human learning http://www.theguardian.com/technology/2014/mar/21/zuckerberg-invest-startup- brain-software-vicarious • Computer with human-like learning will program itself http://www.newscientist.com/article/mg22429932.200-computer-with-humanlike- learning-will-program-itself.html#.VLQccHs5XUs • Google’s Grand Plan to Make Your Brain Irrelevant http://www.wired.com/2014/01/google-buying-way-making-brain-irrelevant/
  • 32. References II • The Race to Buy the Human Brains Behind Deep Learning Machines http://www.businessweek.com/articles/2014-01-27/the-race-to-buy-the-human- brains-behind-deep-learning-machines • Smarter algorithms will power our future digital lives http://www.computerworld.com/article/2687902/smarter-algorithms-will-power- our-future-digital-lives.html • What We Know About Deep Learning Is Just The Tip Of The Iceberg https://wtvox.com/2014/12/know-deep-learning-just-tip-iceberg/ • 10 Signs You Should Invest In Artificial Intelligence http://www.33rdsquare.com/2014/10/10-signs-you-should-invest-in.html • Towards Intelligent Humanoid Robots http://www.33rdsquare.com/2013/02/towards-intelligent-humanoid-robots.html • The Deep Mind of Demis Hassabis https://medium.com/backchannel/the-deep-mind-of-demis-hassabis- 156112890d8a4a • Google isn’t the only company working on artificial intelligence, it’s just the richest https://gigaom.com/2014/01/29/google-isnt-the-only-company-working-on- artificial-intelligence-its-just-the-richest/
  • 33. Bibliography • Barrat, James, Our Final Invention, St. Martin's Griffin, 2014 • Bengio, Yoshua et al, Deep Learning, MIT Press, 2015 • Brynjolfsson, Erik and Andrew McAfee, The Second Machine Age, W.W. Norton & Co., 2014 • Byrne, Fergal, Real Machine Intelligence, Leanpub, 2015 • Ford, Martin, Rise of the Robots: Technology and the Threat of a Jobless Future, Basic Books, 2015 • Kaku, Michio, The Future of the Mind, Doubleday, 2014 • Kurzweil, Ray, The Singularity is Near, Penguin Books, 2006 • Kurzweil, Ray, How to Create a Mind, Penguin Books, 2013 • Nowak, Peter, Humans 3.0: The Upgrading of the Species, Lyons Press, 2015 • Russell and Norvig, Artificial Intelligence, A Modern Approach, Pearson, 2009 • Yampolskiy, Roman - Artificial Superintelligence, A Futuristic Approach, CRC, 2015
  • 34. Questions “A company that cracks human level intelligence will be worth ten Microsofts” – Bill Gates.