Lecture 4 Interviewing 101 Moore s law and Technology evolu;on. October 5, 2017
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1 Lecture 4 Interviewing 101 Moore s law and Technology evolu;on October 5, 2017
2 Today Discussion of HW Homework assignment- Remember to Phillip and myself your HW by noon on the day of class Interviewing Techniques Choreographed Technology Change? Moore s Law
3 Homework for Today- discussion Give name for Team Roles and Responsibili;es Means of communica;on Give Ground Rules for your team Give topic choice (and back-up?) Begin secondary research to scope project If the topic is not interes;ng switch!
4 Homework for Today Give name for Team Roles and Responsibilities Means of communication Give Ground Rules for your team Affirm topic choice (and back-up?) A or B Begin secondary research to scope project What are some of the key questions you would like to answer If the topic is not interesting switch!
5 HW for Next Tuesday October 10th Show ~ 3 Hypotheses Show results of ini;al Secondary Research Best Sources What did you find out that was surprising? List of Poten;al interviewees- Why would they be valuable to talk to?
6 Research Process 1. Decide on project/team 2. Alloca;on of Responsibility who what when 3. Create hypotheses 4. Secondary Research 5. Primary Research 6. Validate or change hypotheses 7. Analysis 8. Write-up
7 Secondary Research Where to find it? Google, Internet Patent search find alterna7ves, compe7tors Scien7fic Publica7ons search Current Events related to technology Economist, newspapers Stock market/financial stmt analysis Industry journals Check out imitators tag-along industries Company news release Use Caltech Research librarian (to be assigned) Consultant reports ask Library for help reimbursement available
8 Primary research Network! Exploit the Caltech connec;on! Customers/Users of Technology Alumni contacts Family contacts Industry analysts Scien;fic/engineering/Industry Conferences and Proceedings Companies/Ins;tu;ons involved in Technology (Research Labs) Consultant Reports Compe;;ve Technology Companies
9 Interviewing techniques How many people? At least 2 Set up at interviewee s convenience Break up roles: Main Person asking ques;ons (Leader) Person Takes Notes (recorder) also can audio record it Observer notes nuance, body-language, asks addi;onal ques;ons Go in with a script ques;ons want to answer Update based on results before next interview How fast should you recap and write down conclusions? Right away!
10 Remember We know about the learning curve
11 But there is also... The Forgefng Curve
12 Interviewing techniques Inform them also what you want to ask about Work around their schedule. Ask for a modest amount of ;me Face-to Face is best! Skype video conference Telephone interview op;on Freeconference.com access # At end of discussion,always ask them who else you should speak to! Follow up with thank-you , that evening You should start on arranging appts ASAP This is the rate-limi;ng step!
13 Biggest E/ME 103 Failure mode?
14 Biggest E/ME 103 Failure mode? Too few interviews!
15 Opinion changes x Opinion x x x x x x x x Number of interviews
16 Choice A Technology Analysis S-curve analysis Delphi (consul;ng wise people ) experienced veterans successful caltech startups investors consultants government journalists and blogists people being displaced analogous situa;on professors Trend extrapola;on Scenario development Reasoning from analogy (the experience of similar technology innova;ons in similar markets) comparing with technology development models and norms. Your own techniques
17 S curve analyses Where are you on the curve Number of compe;tors (growing, shrinking, sta;c) Rate of increase in technology capability Rate of increase in sales/number of customers Number of Publica;ons Market growth History of predic;ons in this space Five years off Key markets
18 Trend Extrapola;on Whatever? T
19 Delphi Choice of experts who are they? What makes them experts? Distribu;on Wri;ngs Interviews Triangula;on/ Correla;on Voice in the wilderness Listen to Assump;ons Groupthink- your own judgment
20 What underlying force(s) drives Technology change?
21 Moore s Law "The number of transistors incorporated in a chip will approximately double every 24 months."--gordon Moore, Intel co-founder
22
23
24 Microprocessor Transistor Counts & Moore's Law 2,600,000,000 1,000,000,000 Six-Core Core i7 Six-Core Xeon 7400 Dual-Core Itanium 2 AMD K10 Itanium 2 with 9MB cache POWER6 Itanium 2 AMD K10 16-Core SPARC T3 Core 2 Duo Cell 10-Core Xeon Westmere-EX 8-core POWER7 Quad-core z196 Quad-Core Itanium Tukwila 8-Core Xeon Nehalem-EX Six-Core Opteron 2400 Core i7 (Quad) 100,000,000 AMD K8 Pentium 4 Barton Atom Transistor count 10,000,000 1,000,000 curve shows transistor count doubling every two years AMD K5 Pentium AMD K7 AMD K6-III AMD K6 Pentium III Pentium II 100, ,000 2, Z MOS RCA Date of introduction By Wgsimon - Own work, CC BY-SA 3.0, hmps://commons.wikimedia.org/w/ index.php?curid=
25 10th September 2013 Intel reveals 14nm PC, declares Moore's Law alive and well
26 Technological Capability Capability Year
27 NAND Flash Memory (wikipedia)
28 Technological Capability Capability Year
29 Technological Capability Capability Year
30
31 Internet Hosts Count
32
33 Hard drive prices Frys.com Six years ago at 500 $ or $0.32/GB Five years ago 750 $ or $0.20/GB Three Year Ago 2 $ or $.06/GB Two years ago $ or.06/gb (unchanged) Yesterday $ or.04/gb Amazon Yesterday $ or.025/gb
34 hmp:// v=frlgslqe9f8 hmp://
35 Very Rough Cost of storing Levi s first steps 100 Kbyte/s x 60 sec =6 Mbytes Say 1 Mbyte with compression Cost of storage space = 4 milicents And that s retail
36 Moore s Law and it s Fallouts What drove these drama;c changes to occur over decades? Demand Learning curve Industry wide coopera;on Upstream technologies G;lobal compe;;ve forces Sef fulfilling prophecy
37 What about biology? Are there Moore s Laws opera;ng in Biology?
38 What about biology? Are there Moore s Laws opera;ng in Biology? Exponen;al scaling of single-cell RNA- seq in the last decade Valen;ne Svensson1, 2,*, Roser Vento-Tormo2, Sarah A Teichmann1, 2 The ability to measure the transcriptomes of single cells has only been feasible for a few years, and is becoming an extremely popular assay. While many types of analysis and ques;ons can be answered using single cell RNA-sequencing, a central focus is the ability to survey the diversity of cell types within a sample. Unbiased and reproducible cataloging of dis;nct cell types requires large numbers of cells. Technological developments and protocol improvements have fuelled a consistent exponen;al increase in the numbers of cells studied in single cell RNA-seq analyses. In this perspec;ve, we will highlight the key technological developments which have enabled this growth in data.
39 What about biology? Are there Moore s Laws opera;ng in Biology?
40 What about biology? Are there Moore s Laws opera;ng in Biology? Figure 1: Scaling of scrna-seq experiments (A) Key technologies allowing jumps in experimental scale. A jump to ~100 cells was enabled by sample mul;plexing, a jump to ~1,000 cells by large scale studies using integrated fluidic circuits (IFCs), followed by a jump to several thousands using liquid handling robo;cs. Further order of magnitude jumps were enabled by random capture technologies through nanodroplets and picowell technologies. Recent studies have employed in situ barcoding to reach the next order of magnitude. (B) Cell numbers reported in representa;ve publica;ons by publica;on date. Key technologies and protocols are marked, and a full table with corresponding numbers is available in
41 What about biology? Are there Moore s Laws opera;ng in Biology? DNA sequencing cost per base pair greater than Moores Law Imaging technologies Neurons simultaneously recorded Problem of changing platorm
42 What else? Robo;cs? AI? Transport? Military? Educa;on?
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