Archive for the ‘Lemelson-MIT Prize’ Category

17th Annual EmTech @ Media Lab, MIT – November 7 – 8, 2017, Cambridge, MA – This Year’s Themes, Speakers and Agenda

MIT Media Lab
Building E14
75 Amherst Street 
(Corner of Ames and Amherst)


  • Business Impact
  • Connectivity
  • Intelligent Machines
  • Rewriting Life
  • Sustainable Energy
  • Meet the Innovators Under 35

Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston


will cover in REAL TIME

The 17th annual EmTech MIT – A Place of Inspiration, November 7 – 8, 2017, Cambridge, MA


MIT Technology Review’s EmTech conference


In attendance, covering LIVE using Social Media

Aviva Lev-Ari, PhD, RN











  • 8:00
    Registration & Breakfast
Opening Remarks – Elizabeth Bramson-Boudreau, MIT TR
  • In Media Lab – MIT and MIT Technology Review was established in 1899
  • EmTech 1999 – 100 years to MIT Technology Review
  • Innovations and pushing the boundaries
  • AI – potential and limitations
  • Climate change requires new technologies
  • Brain Technologies: Biology Vision
  • Tomorrow: emerging technologies: Cybercrime, role of technology
  • Automation and future of work
  • Partners: GE, Lamburghini
  • Lemelson-MIT
  • MITTR – Whova on AppleStore
The State of AI – Andrew Ng, CS.AI, Stanford University – was 2008 Young Innovator,
Founder, Deeplearning.ai; Adjunct Professor, Stanford University
  • Trends in AI – AI is the new Electricity
  • Deep Learning & Neural Networks (NN):
  1. Input a picture –>> output: Is it You?
  2. loan application outcome: will you repay (%)
  3. picture from car – Output GPS address –>> Supervised Learning
  4.  doing act in <1 sec of thinking
  5. training SMALL, Medium size very large NN
  6. Algorithm innovations:

Supervised Learning algorithm types:

  • Transfer Learning
  • Unsupervised learning
  • Reinforcement learning – hunger for data: i.e., robotic applications

Importance of Data accumulation for launch a Product –  Users — data growth

  • Shopping Mall + website is not equal an Internet company
  • Internet company:
  1. push data to CEOs
  2. A–B Testing
  3. Short cycle time
  4. Decisions made by PM and ERP

AI era

traditional company + NN not equal AI company

  • Strategic data acquisition
  • Unified data warehouse
  • Precision automation
  • ORGANIZATION CHART to interface in a matrix with AI Teams – hire Ai in the Business Units
  • Scarce talent of AI


  • Children MUST learn to code
  • Human-Computer communication will be by writing code
Meet the Innovators Under 35
  • Future of work
  • warranted reliant digital connectivity
Break & Networking
AI’s Next Leap Forward

Tomasso Poggio, MIT, CSAIL, BCS

  • Deep learning  – next step
  • Bet on Center Brain Mind Machines (CBMM)
  • Josh Tennenbaum at MIT
  • Autonomous Driving – Amnon Shaashua, MobilEye
  • 20 years @ MI AI: Dailmer and MIT — detection of pedestrans
  • Powerful computers and algorithms – Reinforcement Learning Networks (Brain Science), models of Vision and Deep Learning Networks – WHEN they work?
  • Building Jarvis – a buttler application in AI built by Marc Zukenberg
  • NeuroScience – MobilEye, AlphaGo
  • CBMM – NSF $50 Million in AI funding  – Science of Intelligence and Engineering of Intelligence
  • MIT & Harvard plus several organization
  • Business Partners: MS Soft, Google bought MobilEye,
  • Center for Visual Gaze – 200 msec of visual processing
  • ERGO SUM: toward symbols, Cognitive core, visual system, Brain OS – running routines
  • Breakthroughs: Theory: under which conditions,
  1. Learning theory
  2. optimization Approximation Theory: Deep vs Shallow networks
  3. Intelligence is greatest problem to solve it is like LIFE, Tomasso Poggio, MIT, CSAIL, BCS
  4. machine can help human to think better, long time horizon is needed,

Kris Hammond, Prof. Northtwestern University

  • Data analytics and Ideas
  • words vs language – past, present, future – uniquely HUMAN, now machine language is Human Partner
  • language vs Ideas
  • machines knows a lot
  • facts, dat move to narrative
  • Language is understanding
  • FIN information: Decisions about allocations,
  • Turbidity data on the beach in Chicago: Which Beach is the cleanest vs the dirtiest
  • NARRATIVE ANALYTICS: data that machine can tell us what it has as a story and presented as intelligent language,

    Cognitive Science application to autonomous driving – Yibio Zhau, Tennenbaum Lab @MIT, ISEE.AI, Computer vision, Cognitive Science

  •  interpulate and extrapulate data needed for autonomous driving
  • reasoning beyond the system: Human intelligence , intentional reasoning, pattern recognition,
  • Ali baba – funding building of a Robot for autonomous driving – understanding by imagining – causes for behavior by others
  • ISEE – Next generation of AI — driving drivessless ly for thousands of miles
  • Car to car communication is a sensoring issue, negotiation need to be taught to machines

Young Scientists 35 years old or less

Austin Olson, Luminar – object detection 99% accuracy,

Angella Schoellig — Roborts, Prof. University of Toronto, robots in predictable environments

Lorenz Meier — Vertical Technologies – Drones and safety – DB of flights

Lunch & Networking
Adapting to the reality of climate change

Lee Krevat, Sempra –

owns Wind Farms- managing a Grid with renewable energy. Variable – Wind technology wind is variable – if wind blows too much switch to diesel. 100% renewable for one hour on Islands

Growth area:
  1. 20 cents diesel, wind is 10 cents help the enviroment

mainland, not yet used, price diesel vs wind

Solar wind generation – next biggest Technology in Energy


Alex Tepper,Avetars

Robotics, Drones, AI and the Future of Energy – A start up incubator sponsored and funded by GE
  • RAIL – Predict derailments
  • OIL & GAS – corrosion is the enemy — knowledge of corosion progression – using AI algorithms

Growth area: Aviation

John Holdren – Harvard University – Government  Role in ENERGY and Climate Change – Obama’s advisor Presidential CSO on Climate and energy

  • mitigation
  • adaptation
  • suffering – shortcomings of mitigation and adaptation
  • harm of business as usual
  • Efficiency standards during Obama Administration, assistance to other countries led to the Agreement in Paris 195 countirs — agreement to reduce emission. China and US declare cooperation on emission of gases into the environment.
  • All executive orders by Obama – were reverted by Trump
  • Innovations: Electricity from Solar increase and wind as well and batteries
  • Carbon capture and storage – technological challenge
  • Biofuel processing, liquid bio fuel
  • Nuclear innovations to nuclear waste
  • 2100 – 5% on defense and 2% on the environment – model under estimate the contribution of innovations for the long run.
  • 1000 businesses in deployment of technologies

Evelyn wang, MIT – Material Science – Sustainable energy – nano

  • material properties: superior properties of LOW DENSITIES
  • Light manipulation
  • membrane
  • CO2 capture
  • Technologies: Nnao, Thermoelectronics, energy and water
  • Solar 6% and wind 21%, biomass 5%voltaic
  • SOlar eneconversion
  • Nanophotonics: Solar energy conversion: photo
  • Nano absorber – area ratio; Emitter: silicon and silicon  – spectral approach
  • potential STPVs
  • Transportation using energy with emission
  • Power consumed by HVAC
  • Thermal Battery for Electric Vehicle: Adsorption Heating and Cooling
  • Desorption vs Adsoption: cooling vs Heating mode
  • High capacity adsobents – Zeolite  MOF enhancing capacity heat and mass transport
  • Tmal Battery Prototype: Hybrid, electric, stationary domestic HVAC.
  • Water harvesting from Air – metal organic Frameworks: Adsorption – harvest water without need of additional electricity
  • Opportunities for Advanced Materials

Prof. David Keith, Harvard University

  • technologies to stop global changing
  • research program
  • stratospheric aerosol cool planet – pollution masking global warming
  • solar geo-engineering, vs emission cut 3x BAU vs business as usual
  • Annual maximal Temperatures, extreme precipitation,
  • carbon emission worm up vs climate risk in Time
  •  use of technology for climate change mitigation: carbon removal
  • Solar engineering is the solution
Break & Networking
Meet the Innovators Under 35

Next Generation Brain Interfaces

Andrew Schwartz, University of Pittsburg

  • Causality is obscure
Lemelson-MIT Prize Honors & Reception
Lemelson-MIT Prize Honors Feng Zhang, MIT with the Prize for contributions to CRISPR Applications as a therapeutics method in genomics


  • 8:00
    Registration & Breakfast
9:00 Elizabeth Branson
9AM – 9:30AM Robots and AI in Everyday Life

Daniela Rus, CSAIL, MIT – Robots: drones, 3D Printing

hosted by David Rotman, MIT TR

  • supply chain and transportation – city will benefit from a different business model
  • autonomous driving deployed in Singapore
  • all vehicles on wheels can be made autonomous
  • blind – camera on a belt assists in navigation
  • ML: Patterns and predictions
  • AI – reasoning
  • robots: motion
  • Machine read entire libraries
  • Radiology: Read by machines vs by Radiology: AI  + Human — 0.5% error
  • Rural area medicine
  • Machines – Better Lawyers: NLP – read precedents to cases, machines can’t write a briefing or defend a plaintif
  • Factory and Automation: Robots roles – enable mass OPTIMIZATION  not only mass production
  • Machines do not have common sense and do not have ability to reason
  • crunching data vs analysis
  • JOB Categories:Tasks vs Professions: Routine data processing and labor task — are ready for automation
  • NEW jobs: User experience designer, GPS enable taxi drivers to drive and drove pay scale down
  • GDP – decreased 1966 – 2016
  • KY school to train coal miners to do data processing to become CODERS
  • JFK – new machines brings man back to jos – new jobs
  • AI supports NEW jobs: CS/AI part of literacy
  • people and machines – in collaboration


  • Who to make the transition?
  • CODING is key – people must be active in keeping up and continue to train
  • make it easy to make machines, interactions Man-Machine easier,
  • YOU ARE WRONG SIGNAL IS recognized by EEG
  • AI and Future of Work Conference at MIT – anxiety related to job changing due to technology
  • Technology can’t solve all problems, Technology helps, Technology implications on Policy – technology as a unifier societal force not a dividers
  • Transportation as Utility

9:30 – 10:00 AI and the Future of Work

Iyad Rahwan, MIT Media Lab, Introduction by Elizabeth Woyke, TR

  • Physical Therapist — will not be replaced by computerisation
  • Probability of computerisation: Skilled cities are better at economics shocks
  • Adam Smith – simple operations
  • Differential Impact from Automation on Cities – the larger the city more resilient to automation
  • City size vs clusters of occupations — cluster grow with city size
  • Impact on Middle Class vs Lower and Upper: low paying jobs, middle and high
  • Skills in Occupations: mapping SkillScape correlations with Education
  • Skills in demand


  • Urbanization took place – 80% live in cities around the World
  • Outliers in CIties by size and Skills: Boulder, CO – small size very skilled labor, politics support start ups and high tech

10AM – 10:30AM

Meet the Innovators Under 35

  • Tracy Chou – ProjectInclude – diversity
  1. All about data
  • Olga Russakovsky – Princeton University – Computer vision
  • AI for education of under privileged high school
  1. IM-GENET – Data sets encode human biases
  2. AI is powered by Data
  3. AI learns societal  biases
  4. Researchers shape AI
  • 10:30

    Break & Networking

  • 11:00 – 11:30 What is Social Media Doing to Society?
 Yasmin Green, Jigsaw, Google
  • 300 million reach of Ads posted by Google in the Internet
  • Fake news
  • Network shape
  • Veracity and popularity personalized
Hosted by Martin Giles, TR
  • e 11:30 – 11:45 Meet the Innovators under 35
  • Phillipa Gill  – UMass CS – Project of Network measurement on censorship measurement platforms
  • Joshua Browder – DoNotPay

11:45 – 12:00 The Emerging Threat of Cybercriminal AI

Shuman Ghosemajumder, Shape Security

Hosted by Martin Gile

  • CyberCrime is evolving using AI – Imitation Game – Turing Test restricted Turing Tests
  • Computer vision, Solving CAPUTRE – Copletely Automated Turing  Tests
  • CAPTCHA by Google
  • Credential Stuffing Accounts Attacks – SONY was hacked and 93,000 Passwords stolen
  • Clip Farms at Google
  • BLACKFISH – identify Credential Stuffing Accounts Attacks, all invalid password are not valid to be used by cyber attackers again – that authentication is no longer valid
  • Multi Factors Authentications vs ease of use to Log In
  • Knowledge Basis – Probabilistic  SYmbols – BlackFISH – technological advantage – iPhone stores a math formulation of characteristics of the finger print not the image of the fingure
  •  12:00
    Lunch & Networking – Lamborghini -super sport car
1:30 – 2PM
Technology Spotlight: Mind-Controlled VR
Ramses Alcaide, Neurable
Hosted by Rachel Metz, TR
  • Killer Platforme ==>Killer Interaction ==>Killer application
  • Reactive ==> Proactive
  • Brain Computer Interaction (BCI) – maximum Privacy no voice involved like in SPeech
  • Voice, Motion Tracking, eye tracking
  • Human intentionality – a World without limitations
  • NASA is a client
  • consol technology for navigation, typing,
  • Problems: Add to glasses or as an Ear piece
  • the signal is ACTION POTENTIAL
  • latency differences between individuals
  • Non-invasive to invasive to capture signals
2:00 – 2:30 Capturing Our Imagination:: Evolution of Brain-Machine Interfaces
Mary Lou Jepsen, Openwater
Hosted by Antonio Ragalado, TR
  • Using functional MRI technology for a NEW device to scan emotions rather than medical diseases
  • HOLOGRAPHY of the Brain – liquid crystal display is like transistors on a chip
  • OPTICS – DISCONTINUITY of Moore’s law – high resolution like functional MRI
  • Holographic LCD – scattering material VOXEL detector – measure intensity of light, no resolution, consumer camera speed OK Inexpensive
  • Human body scattering
  • HAT and Bandage
2:30 – 3PM Future of Work – REWARD DISOBEDIENCE –
New Prize of $250,000  – Ethics and governance in AI at MIT Media lab
Reid Hoffman, Greylock Partners Founder LinkedIn
conversation with Joi Ito, MIT Media Lab
  • Tell the Truth
  • Media Lab — a Non-disciplinary place
  • Universities play a role in Social Justice
  • FEAR of AI:
  1. For profit will own it all
  2. stupid AI will govern
  3. displace work
  4. espionage
  5. catalytic institute that will make a contribution to OPENNESS vs technological dominance

Joi Ito, MIT Media Lab: AI problems –

  • MUST be democratized – Now it is in the hands of very FEW
  • RISK SCORES can’t be contested in court because they are IP of for profit companies
  • Joi Ito, MIT Media Lab at MIT do good to Society vs make the most of money which the majority are doing
  • AUTONOMICH vs autonomous agents, said Joi Ito, MIT Media Lab – Hoffman: Design goals more symbiotic: Scaling, more productive, Season 2 launched today
  • Design principle – LEARNING vs EDUCATION, Joi Ito, MIT Media Lab

Hoffman on AI Technologies

  • shaping it to avoid catastrophic negatives
  • provide a public good via participation
Break & Networking
3:30 – 4 Big Problems, Big Data Solutions
Deb Roy, MIT media Lab
  • Tweets and News, Washington Post – Tracking tweets from US on Politics related to the Elections
  • National memory on Guns, Immigrations
  • Debate brief from tweets and News rooms
  • topic classifier,  Campaign finance, SHARE OF COVERAGE IN NEWS, SHARED OF VOICE ON TWITTER
  • deep neural network training algorithms
  • Passion Gap: cut data on Twitter – Trump supporters exhibited x2 fold energy vs the Democratic candidate
  • How does Media flow: Sanders, Clinton, Trump – each is a Media Source
  • Truth, Trust, Attention  – Fact checking
  • If Trust the source then I believe it is True
  • Public Opinion: The Politics of Resentment in Rural WI – Katherine Cramer
  • Listening Networks: Human- Human Interaction: Media sharing network – change week by week – the MOST innovative methodology developed to date for Public Opinion – presentation by
    Deb Roy, MIT media Lab  – using deep Neural network training
  1. main stream
  2. conservative
  3. liberal activist
  • Health Indicators:
  • Shared attention
  • Shared Reality
  • Varied Perspective – surface under-heard voices
3:30 – 4
Meet the Innovators Under 35
1. Svenja Hinderer, Germany
  • Valve – development of Tissues, biochemical properties
  • signaling molecules
  • mechanical strength – physiological
  • Attrach stem cells – proper matrix formation
  • Functional implants
2. Viktor Adalsteinsson
  • Cancer Precision medicine – Liquid biopsy – tumor mutations
  • entire Cancer Genome – from blood biopsy
  • Scaling: Broad Institute 100 collaborators – 3,000 blood sample genomical analysis
2.Tallis Gomes, CEO Entrepreneur, Brazil
  • Easy Taxi
  • Fighting inequality
  • 15Billion – Beauty Market
3. Abidigani Diriye
  • IBM Research Africa – 300 million adults – lack of access to financial services
  • Univesities, Government  – start ups to scale ideas
Eyad Janneh
  • 5:00
    2017 Innovator Under 35 Awards & Reception
  1. Speakers
    • Viktor

      Group Leader, Broad Institute of MIT and Harvard

      2017 Innovator Under 35


    CEO, Sila Nano

    2017 Innovator Under 35

    • rechargeable battery
    • new class of materials charge and discharge in battery
    • store more energy
    • more better designed electronics: electrified flight, solar, car: Hybrid and electric
    • 21st Century belongs to electrification vs combustion in the 20th century,


      CEO, Sila Nano

    • Tracy

      Founding Advisor, Project Include

      2017 Innovator Under 35

    • Adrienne

      Software Engineer, Google

      2017 Innovator Under 35

    • Phillipa

      Assistant Professor, University of Massachusetts, Amherst

      2017 Innovator Under 35

    • Tallis

      CEO, Singu

      2017 Innovator Under 35


    CEO, WafaGames

    2017 Innovator Under 35

    • Ian

      Staff Research Scientist, Google Brain, development occurred at OpenAI

      GAN’s – Generative Adversarial Network – from AI Optimization to Game Theory

      2017 Innovator Under 35

    • Yasmin

      Director of Research and Development, Jigsaw at Google

      Addressing Online Threats to Global Security

    • Kris

      Chief Scientist and Cofounder, Narrative Science

      AI’s Language Problem

    • Svenja

      Scientist, Fraunhofer IGB

      2017 Innovator Under 35

    • Reid

      Cofounder, LinkedIn; Partner, Greylock Partners

      The Future of Work

    • John

      Professor, Harvard University

      Climate Disruption: Technical Approaches to Mitigation and Adaptation

    • Joi

      Director, MIT Media Lab

      The Future of Work

    • Mary Lou

      Founder, Openwater

      Capturing Our Imagination: The Evolution of Brain-Machine Interfaces

    • David

      Professor, Harvard University; Founder, Carbon Engineering

      The Growing Case for Geoengineering

    • Neha

      Cofounder and CTO, Confluent

      2017 Innovator Under 35

    • Andrew

      Founder, Deeplearning.ai; Adjunct Professor, Stanford University

      The State of AI

    • Tomaso

      Investigator, McGovern Institute; Eugene McDermott Professor, Brain and Cognitive Sciences, MIT

      Understanding Intelligence

    • Olga

      Assistant Professor, Princeton University

      2017 Innovator Under 35


    Marie Curie Fellow, EPFL

    2017 Innovator Under 35

    • disruptive technology in the energy space
    • Gang

      Chief Scientist, Alibaba

      2017 Innovator Under 35

    • Jianxiong

      Chief Executive Officer, AutoX, Inc.

      2017 Innovator Under 35

      CAMERA-first solution affordable self-driving


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