Types of Blockchain, AI and its future
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Types of Blockchain - permissioned vs. permissionless platforms Types of AI - Unsupervised, Supervised and Reinforcement Learning, Deep Learning Future of Blockchain and AI
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Types of Blockchain, AI and its future
- 1. Who said this? 1 Mark my words, AI is far more dangerous than nukes On what he’s afraid of “I’m really quite close to the cutting edge in AI and it scares the hell out of me. It’s capable of vastly more than almost anyone knows. And the rate of improvement is exponential. ... We have to figure out some way to ensure that the advent of digital superintelligence is one which is symbiotic with humanity. I think that’s the single biggest existential crisis that we face, and the most pressing one. ... Mark my words, AI is far more dangerous than nukes.”
- 2. Where do you stand on Tech Singularity? 2
- 3. AI & BLOCKCHAIN Aarthi Srinivasan Director of Product Management Target Inc. 3
- 4. Successful if we discuss 1. Introduction to AI 2. Types of AI 3. Introduction to Blockchain 4. Types of Blockchain 4 Goal: Provide a view on the intersection of AI & Blockchain
- 5. AI is not new - Why now? 5 Ref – McKinsey Co, MIT Lex Fridman, HBR, 1. Computing scale: CPU, GPU, ASICs 2. Datasets and infrastructure to handle big data 3. Amazon, Google, FB, MSFT investing in platforms
- 7. 7 ARTIFICIAL INTELLIGENCE The capability of a machine to imitate intelligent human behavior MACHINE LEARNING 1. Getting computers to learn or recognize something without being explicitly programmed DEEP LEARNING Type of ML that can process a wider range of data resources, requires less data preprocessing by humans Let’s get on the same page
- 8. 8 Types of AI MACHINE LEARNING Supervised Learning Unsupervised Learning Reinforcement Learning DEEP LEARNING Convolutional Neural Network - CNN Recurrent Neural Network - RNN
- 9. 9 Types of ML: Unsupervised Learning Algorithms DESCRIPTIVE Look into the Past PREDICTIVE Understand the future PRESCRIPTIVE Advise on outcome UnsupervisedAlgorithms Hierarchical clustering • K-Means • Gaussian • Hierarchical clustering Recommender Systems Use cases • Segment customers in groups based on characteristics such as age, income, interests • Cluster loyalty-card customers into microsegments • Segment customers based on latent preferences for campaigns and promotions • Segmentation based on likelihood of employee attrition • Social media keyword based clustering • Movie, Items, News recommendations based on preferences & interests Ref – McKinse.comy, https://halobi.com/blog/descriptive-predictive-and-prescriptive-analytics-explained/
- 10. 10 DESCRIPTIVEAND PREDICTIVE ALGORITMS EXAMPLE USE CASES • Linear regression • Price of a diamond by its shape, size, clarity etc. • Understand factors contributing to product sales such as prices, advertisements.. • Optimize price points and estimate price elasticity. • Logistic regression (Classification) • Classify diagnosis as benign or malignant • Classify customers as will payback loan or default • Linear quadratic / discriminant analysis • Client churn prediction • Sale closing probability • Decision tree (can be regression of classification model) • Understand salient product attributes contributing to purchase • Provide decisioning framework for hiring / health questions • Naïve Bayes (Classification based on probability) • Analyze sentiment to analyze product perception • Create classifiers to filter spam • SupportVector Machine (SVM) • Predict patients per hour • Predict likelihood of ad clicks • Random Forest • Predict call volume for staffing decisions • Predict power usage in an electrical-distribution grid • Adaboost • Detect credit card fraudulent activity • Simple low cost image classification e.g. sat images for climate change) • Gradient-boostingTrees • Forecast product demand and inventory levels • Predict price of cards based on characteristics such as mileage • Simple Neural Network • Predict a patient’s likelihood to join a healthcare program • Predict conversion of trial users to paid users Types of ML: Supervised Learning Reference: https://www.mckinsey.com, https://halobi.com/blog/descriptive-predictive-and-prescriptive-analytics-explained/, wiki, Papers
- 11. 11 Types of ML: Reinforcement Learning PRESCRIPTIVE USE CASES Advise on outcome Reinforcement Learning - algorithm receives reinforcing rewards for its positive actions (e.g. portfolio optimization) • Optimization of trading strategy for options trading • Stock and pick inventory – robotics • Optimize real time pricing for an online auction • Balance electricity load in grids based on demand cycles • Optimize self-driving car behavior • Optimize driving routes in cars Ref – McKinse.comy, https://halobi.com/blog/descriptive-predictive-and-prescriptive-analytics-explained/
- 12. 12 Types of Deep Learning USE CASES Convolutional Neural Network A multilayered neural network designed to extract increasingly complex features of the data at each layer to determine the output Used to infer data from unstructured data e.g. images Complex Image recognition for: 1. Medical Scans 2. Manufacturing defects 3. Website & video game image monitoring 4. Human sentiment / communication through images 5. Ariel image surveillance Recurrent Neural Network A multilayered neural network that can store information in context nodes, allowing it to learn data sequences and output a number or another sequence Used for time series or sequences such as audio or text Sequenced data uses such as: 1. Language translations 2. Chat bots 3. Aerial surveillance sequences with CNN 4. Narratives for reports (Narrative Sciences) 5. Communication tips & Captions Ref – McKinse.comy, https://halobi.com/blog/descriptive-predictive-and-prescriptive-analytics-explained/
- 14. 14
- 15. What is a Blockchain? 15 • A blockchain is a growing list of digital records or blocks that are secured and linked. Each block contains • Hash value link to the previous block • timestamp and • Data • Genesis block by Satoshi Nakamoto (Bitcoin paper) – 3rd Jan 2009 “TheTimes 03/Jan/2009 Chancellor on brink of second bailout for banks“ • Eliminate the intermediaries • Creates a decentralized system
- 16. 16 ABC’sTX VERIFIED BLOCK JohnTX & more newTxs Nonce Prev block reference / Previous hash Timestamp Example Bitcoin Blocks NewTX 1 NEW BLOCK NewTX 2 & more newTxs Nonce Prev block reference / Previous hash Timestamp …...
- 17. Types of Blockchain 17 PERMISSIONLESS PERMISSIONED • Anyone can participate and validate a block • Common validation method proof of work • Restricted actors can validate a block • Various methods of consensus are used e.g. Byzanthine fault tolerance
- 18. 18 Blockchain Technology Platforms BITCOIN ETHEREUM HYPERLEDGER R3 CORDA Verification Proof of work Data format: Merkle tree (20 txs per sec) Proof of work Data format: Patricia tree Consensus based – Modular & Extensible Consensus with Financial sector as focus area Permission State Permissionless with basic contracts Permissionless with smart contracts (e.g. solidity) Permissioned with smart contracts (e.g. Golang, Java) Permissioned with Smart contracts (Kotlin, Java) Smart Legal prose Distributed Distributed system with all accounts equal access Distributed system with all accounts equal access Distributed system with role-based restricted access Microledgers semi- distributed systems with restricted access Block creators External account External Account, Contract account Multiple roles such as Validator orTransactor Multiple roles including Notary Cryptocurrency Bitcoin currency Ether or other tokens via smart contract No currency (chaincode tokens if required) No currency Ref – McKinse.comy, https://halobi.com/blog/descriptive-predictive-and-prescriptive-analytics-explained/
- 19. AI $37B market by 2025 19 * - 2012 – 2017 ; Ref: Venture beat, $15 B* AI investments with $15Trillion impact on GDP by 2030 Images: Intershala Start ups ~$8B (2012-2016) AutoTech Core AI (Training data) Healthcare 2 31 Big Corporations ~$6B (2013 - 2016) Voice is the newText AI Platform Cloud 1 2 Vision & Image recognition 3 Ford invested $1B in Argo self-drivingAI tech
- 20. Blockchain Future 2 0 Identity, Hardware & Platforms • Identify Platforms • Blockchain standardization Crypto devices & Apps (Crowd data) • Crypto wallets, phones / ipads • Open source apps for crowdsourcing • All your phone apps using Blockchain such as Airbnb, Facebook, Searches, Twitter type applications in Blockchain with token rewards. AI application with smart contracts • Finance protection contracts • Document fraud detection • iOT safety & security (Pollution monitoring, Museum art) • Medical AI contracts • Autonomous cars • Decentralized Organizations (Common Education Standards, Bot shares, Refugee crisis, weaponry) 5 10 Company Token Description Singularity Net AGI Connect SiloedAI algorithms & decentralize (Ben Goertzal OpenCog) Effects.ai Mar 24 - EFX 1st : MechanicalTurk, 2nd :AI Marketplace, 3rd:Compute share Medrec Private Blockchain authenticated by medical researchers to store medical records Loomia - Clothing panel tile that does Lighting, Heating, Sensing data to collect you money
- 21. 21 We will achieve technology singularity with ethics Ref – http://blog.crisman.com image with edits, Business times, Guardian The "Kuratas" robot in Tokyo, Nov. 2012. The military robot can be controlled by a pilot or via a smartphone. It is armed with a futuristic weapons system, including a multi- rocket launcher. Elon Musk & Deepmind’s Mustafa Suleyman leading a group of 116 specialists from across 26 countries who are calling for the ban on autonomous weapons. - 2014
- 22. Sources 22 https://halobi.com/blog/descriptive-predictive-and-prescriptive-analytics-explained/ https://www.nasa.gov/content/dorothy-vaughan-biography https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/an-executives-guide-to-ai https://www.youtube.com/watch?v=1L0TKZQcUtA https://www.engadget.com/2018/02/21/google-ai-ad-placement/ https://www.slideshare.net/saarthi_123/product-school-ai-funding-trends-product-management https://seekingalpha.com/article/4063499-investing-artificial-intelligence-economic-growth-stock-picking https://www.cbinsights.com/blog/artificial-intelligence-startup-funding/ https://www.cbinsights.com/blog/cybersecurity-ai-startups-threat-trends/ https://venturebeat.com/2017/06/09/ais-37-billion-market-is-creating-new-industries/ http://www.nanalyze.com/2017/04/6-ai-cybersecurity-startups/ https://www.cbinsights.com/blog/artificial-intelligence-healthcare-startups-investors/ https://www.cbinsights.com/blog/artificial-intelligence-startups-healthcare/ https://www.wired.com/2017/05/mapped-top-263-companies-racing-toward-autonomous-cars/ http://www.businessinsider.com/the-companies-most-likely-to-get-driverless-cars-on-the-road-first-2017-4/#2-general-motors-17 https://www.techemergence.com/examples-of-artificial-intelligence-in-education/ https://venturebeat.com/2017/03/13/5-tech-firms-racing-to-invest-in-ai-startups/ https://www.cbinsights.com/blog/top-acquirers-ai-startups-ma-timeline/ http://www.cms-connected.com/News-Archive/April-2017/Google-Apple-Facebook-Intel-Microsoft-Salesforce-Twitter-Battle-AI-Supremacy https://www.wired.com/insights/2014/08/the-new-eyes-of-surveillance-artificial-intelligence-and-humanizing-technology/ https://www.movidius.com/news/intel-movidius-helps-bring-artificial-intelligence-to-video-surveillance-cameras https://www.forbes.com/sites/steveculp/2017/02/15/artificial-intelligence-is-becoming-a-major-disruptive-force-in-banks-finance-departments/#52bc03a34f62 https://www.forbes.com/sites/gilpress/2017/01/23/top-10-hot-artificial-intelligence-ai-technologies/#58bb8bf19287 http://www.internetlivestats.com/google-search-statistics/#trend http://blog.crisman.com image with edits https://angel.co/artificial-intelligence https://www.theguardian.com/technology/2017/aug/20/elon-musk-killer-robots-experts-outright-ban-lethal-autonomous-weapons-war http://www.ibtimes.com/un-says-killer-robots-threaten-human-rights-considers-banning-autonomous-weapons-warfare-1584351 https://bornonjuly4.me/2017/01/10/blockchain-what-is-permissioned-vs-permissionless/
- 23. Appendix 23
- 24. 24 Infrastructure , 5, 0% Video editing , 5, 1% Robotics , 15, 2% Image recognition , 22, 2% Keyboard type prediction , 30, 3% Machine Learning Chip , 30, 3% Speech recognition - NLP , 35, 4% Health platform , 44, 5% ML Platform , 50, 5% Vision , 55, 6% Vision processor chip , 130, 14% Core AI , 515, 55% Investment MM Infrastructure Video editing Robotics Image recognition Keyboard type prediction Machine Learning Chip Speech recognition - NLP Health platform ML Platform Vision Vision processor chip Core AI
- 25. Bitcoin protocol steps Refer to my product school bitcoin talk for details on each step Aarthi Srinivasan 25 Start: Broadcast new transaction Verification: User Signature & funds Proof-of-work: Prevent double spending Mining: Earn bitcoin rewards Recheck transactions & start new block 1 2 34 5 Search Engine Account e.g. ABC wants users to use its search engine and will pay them 1 crypto unit(or some fraction of a bitcoin) as a reward in return for using the search engine.
- 26. Key Terms 26 • A set of data used to predict relationships. Data and answers for each sample. • E.g. A diamond’s size, cut, color and clarity helps predicts the price. Training Set • Uses training set to make a prediction. • E.g. Model predicts diamond prices based on past prices.Supervised Learning • Provide data without suggesting anything so computer can identify patterns or groupings. • E.g. Customer segmentation, DNA groupings. Unsupervised Learning • Each distinct measurable data value you select in the training data set. • E.g. A diamonds’ size is one of the feature’s for predicting price. Features/ Variables / Attributes • Using the features provided in the training set make a prediction. Fit a curve using the data provided. • E.g. Price of diamond = X*Cut + Y*Clarity + Z*Size + other features… Supervised: Regression • A defined set of categories that can be labeled for placing new observations. • E.g. Presence of absence of cancer; Types of diabetesSupervised: Classification • Process of assigning observations into subsets. • E.g. Customer segment creationsUnsupervised: Clustering
- 27. 27 Ref – MIT
- 28. Voice will be the new Text 28 Google • 11+ acquisitions • ML Platform creation • Vision / Image and Speech recognition • Business Process improvements Apple • 7+ acquisitions • Vision / Image and Speech recognition • Catch up with Google on platform creation (Turi) Facebook • Vision / Image and Speech recognition • Voice activation SDKs Microsoft • Voice enabled assistant • Type ahead predictor • Voice activation SDKs (AI Fund)
- 29. 29 These companies market cap surpass the GDP of India (previously Russia and Canada) Reference – Scott Gallowa
Editor's Notes
- The technological singularity (also, simply, the singularity)[1] is the hypothesis that the invention of artificial superintelligence will abruptly trigger runaway technological growth, resulting in unfathomable changes to human civilization.[2] - Wikipedia Moore’s law vs. Super-exponential growth: S shaped growth “Ray Kurzweil - Exponential growth is deceptive however, staying almost flat at first until it hits what Kurzweil calls "the knee in the curve" where it then rises on an almost vertical trajectory. In fact, Kurzweil believes evolutionary progress is super-exponential because even more resources are deployed to the ‘winning’ process. So instead of picturing a single growth curve, think instead of a continuous series of ‘S curves’. An example Kurzweil offers in this context is that when vacuum tubes stopped getting faster and cheaper, transistors became popular and continued the overall exponential growth. Moore’s law vs. Super-exponential growth”
- Large data set & processing tools Modern algorithms: Backprop, CNN, LSTM Infrastructure / Software
- Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. Probability of winning board vs. loosing board is calculated 1 What is ML Branch of computer science and AI Lots of data to a computer so they can figure it out Two definitions of Machine Learning are offered. Arthur Samuel described it as: "the field of study that gives computers the ability to learn without being explicitly programmed." This is an older, informal definition. Tom Mitchell provides a more modern definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. 2. It is important to distinguish between big data and machine learning. You need lots of seed data for creating a machine learning algorithm or related algorithms e.g. DNA data We can get this data from just who we are – demographics, psychographics, digital exhaust, our health records, education and the list keeps going. Ok so what – you collect this data and then ML will mine this data, find patterns and model behavior with interactive responses. 1. Amazon’s Dash and 23 &me collect a ton of data about their users which they can put to use for predictions with machine learning. Pillo is the in house health robot Now that we know what it is, let us look at why it is important
- Uses training data and feedback from humans to learn the relationship of given inputs to a given output An algorithm explores input data without being given an explicit output variable Algorithm learns a task simply by trying to maximize rewards it receives for its actions Supervisd: Uses training set to make a prediction. E.g. Model predicts diamond prices based on past prices. Unsupervised: Provide data without suggesting anything so computer can identify patterns or groupings. E.g. Customer segmentation, DNA groupings. Supervised - regression Using the features provided in the training set make a prediction. Fit a curve using the data provided. E.g. Price of diamond = X*Cut + Y*Clarity + Z*Size + other features…
- Blockchain formation. The main chain (black) consists of the longest series of blocks from the genesis block (green) to the current block. Orphan blocks (purple) exist outside of the main chain. Bitcoin network data A blockchain,[1][2][3] originally block chain,[4][5] is a continuously growing list of records, called blocks, which are linked and secured using cryptography.[1][6] Each block typically contains a cryptographic hash of the previous block,[6] a timestamp and transaction data.[7]
- As Nate noted below, there is also a 1MB block size limit which limits how many transactions can be included in a block. Currently over 500K bitcoin blocks
- R3 leads a consortium of 70 banks
- With 15 Trillion impact by 2030 and 15 B in Investments (8 B in startups and 6 – 7B in large companies), we can determine how the funding is distributed. Top 3 areas will be: Auto tech – self-driving cars Training data prep and prediction on open source data using block chain for security (Ayushnet) – Core AI capability Healthcare – Early prevention and diagnosis
- With 15 Trillion impact by 2030 and 15 B in Investments (8 B in startups and 6 – 7B in large companies), we can determine how the funding is distributed. Top 3 areas will be: Auto tech – self-driving cars Training data prep and prediction on open source data using block chain for security (Ayushnet) – Core AI capability Healthcare – Early prevention and diagnosis
- Unite using technology to solve big problems without dehumanizing Google’s Deepbrain has established an AI Ethics group to prevent Skynet terminator use cases or even privacy issues
- His paper talks about the details of the bitcoin and blockchain. I have simplified the protocol or process with 5 steps and will go through each one in detail. Consider a user case where a blockchain based search engine wants users to use its search engine to get the data and rewards them for every search with its tokens. The steps that are followed are: Broadcast transactions i.e. paying the user from search engines account, Verify the user’s signature / funds, verify the user has not double spent the money (paid you and paid himself) using the concept of mining & proof of work and then the final step of rechecking transactions and committing before starting a new block,
- Training set: Data and answers for each row e.g. Diamond prices are determined by its cut, clarity, color and size. In our world - Users propensity to purchase (engagement, Click through, purchase etc) is determined by the context, previous purchases and other parameters. Supervised Learning: Uses a training set to make the prediction for a new observation. For example, you give a new diamond’s cut clarity and size to the model and it will predict a price based on the other past training samples you have provided. Unsupervised Learning: Provide data without suggesting anything so computer can identify patterns or groupings. Next, lets talk about the high level steps in Machine learning.
- Brain is Google’s automated algorithm learning system. RankBrain is used for search queries Business process improvements with Deepmind acquisition e.g. energy savings with data Vision / Speech / Image recognition AI acquisitions Google 11+ acquisitions ML Platform creation Vision / Image and Speech recognition Business Process improvements Apple 7+ acquisitions Vision / Image and Speech recognition Catch up with Google on platform creation (Turi) Facebook Vision / Image and Speech recognition Voice activation SDKs Microsoft Voice enabled assistant Type ahead predictor Voice activation SDKs (AI Fund)