computing, data, algorithms, and, problem solving
TRANSCRIPT
@pieroleo
Computing, Data, Algorithms, and, Problem SolvingPietro LeoExecutive Architect - IBM Italy CTO for Artificial IntelligenceIBM Academy of Technology LeadershipHead of IBM Italy Center of Advanced Studies
A guest lecture prepared for
Information Technology & Digital
Strategy
@pieroleo
www.pieroleo.com
@pieroleo
ALGORITHMSMy dentist told me how to perfectly clean your teeth…..
COMPUTINGMe, a toothbrush, toothpaste and water…
PROBLEMSI have to brush my teeth….
DATATeeth can get sick, caries, tartar…
@pieroleowww.pieroleo.com
@pieroleo
ALGORITHMS PROBLEMSDATACOMPUTING
• QUANTUM LAWS
• DIGITAL
• ANALOG
High Complexity
• WISDOM
• KNOWLEDGE
• INFORMATION
• NUMBERS
• TALK WITH A DIGITAL HUMAN
• DGITAL TRANSFORMATON
• ASK FOR A LOAN
• 2+2=4
• MACHINE LEARNS WITHOUT A TRAINING
• SUPERVISE AND TRAIN A MACHINE
• PROGRAM
LowComplexity
Dimensions of IT systems and their complexity
@pieroleowww.pieroleo.com
@pieroleo
ALGORITHMS PROBLEMSDATACOMPUTING ALGORITHMS PROBLEMSDATACOMPUTING
ALGORITHMS PROBLEMSDATACOMPUTING
1950-1960 1960-1970
1970-1980
ALGORITHMS PROBLEMSDATACOMPUTING
1980-1990
ALGORITHMS PROBLEMSDATACOMPUTING
1990-2000
2000-2010ALGORITHMS PROBLEMSDATACOMPUTING
2010-….
IT Complexity waves
ALGORITHMS PROBLEMSDATACOMPUTING
• QUANTUM LAWS
• DIGITAL
• ANALOG
• WISDOM
• KNOWLEDGE
• INFORMATION
• NUMBERS
• TALK WITH A DIGITAL HUMAN
• DGITAL TRANSFORMATON
• ASK FOR A LOAN
• 2+2=4
• MACHINE LEARNS WITHOUT A TRAINING
• SUPERVISE AND TRAIN A MACHINE
• PROGRAM
@pieroleowww.pieroleo.com
@pieroleo
ALGORITHMS PROBLEMSDATACOMPUTING
• QUANTUM LAWS
• DIGITAL
• ANALOG
High Complexity
• WISDOM
• KNOWLEDGE
• INFORMATION
• NUMBERS
• TALK WITH A DIGITAL HUMAN
• DGITAL TRANSFORMATON
• ASK FOR A LOAN
• 2+2=4
• MACHINE LEARNS WITHOUT A TRAINING
• SUPERVISE AND TRAIN A MACHINE
• PROGRAM
LowComplexity
Dimensions of IT systems and their complexity
@pieroleowww.pieroleo.com
@pieroleowww.pieroleo.com
City
Lifestyle
ZIPcode
CostalvsInland
Generation
Location FamilySize
Gender
IncomeLevelAge
Loyalty&CardActivity
RevenueSize
LifeStages
Education
Legalstatus
Sector
Maritalstatus
@pieroleowww.pieroleo.com
Subscriptions
WishList
SizeofNetwork
Check-ins
Appusageduration
NumberofAppsonDevice
DeviceUsage
Following
Followers
Likes
NumberofHashtagsused
HistoryofHashtagsSearchStringsentered
Sequenceofvisits
Time/Daylogin
TimespentonsiteTimespentonpage
FrequencyofSearch
VideosViewed
Photosliked
Non è possibile visualizzare questa immagine.
@pieroleowww.pieroleo.com
Sentiment
ToneEuphemisms
Hedonism
ExtroversionFaceRecognitionOpennessColloquialism
ReasoningStrategiesLanguageModeling
DialogIntent
LatentSemanticAnalysis
Phonemes
OntologyAnalysisLinguisticsImageTags
QuestionAnalysisSelf-transcendent
AffectiveStatus
12
Imagesource:http://personalexcellence.co/blog/ideal-beauty/
City
Lifestyle
ZIPcode
CostalvsInland Marital status
Generation
Location
Family Size
Gender
IncomeLevel
Competitors
Age
Loyalty&CardActivity
Revenue Size
Life Stages
Eductation
Legal status
Sector
Industry
SubscriptionsDate on Site
Wish List
Size of Network
Check-ins
App usage duration
Number of Apps on Device
Deposits/Withdrawals
Device UsagePurchase History
FollowingFollowers
Likes
Number of Hashtags used
History of Hashtags
Search Strings entered
Sequence of visits
Time/Day log in
Time spent on site
Time spent on page
Frequency of Search
Videos Viewed
Photos liked
Sentiment
Tone
Euphemisms
Hedonism
Extroversion
Face Recognition
Openess
Colloquialism
Reasoning Strategies
Language Modeling
DialogIntent
Latent Semantic Analysis
Phonemes
Ontology Analysis
Linguistics Image Tags
Question Analysis
Self-transcendent
Affective Status
DNA
Proteome
Microbiome
Clinical/Biochemical Data
Steps
Nutrition
Genetics
Runs
X-rays (CT scans) sound (ultrasound), magnetism (MRI), Radioactive (SPECT, PET)light (endoscopy, OCT)
Environment
Bio-Images
Source: Bipartisan Policy Center, “F” as in Fat: How Obesity Threatens America’s Future (TFAH/RWJF, Aug. 2013) @pieroleo
www.pieroleo.com
@pieroleo
Source: http://www.bloomberg.com/video/meet-the-world-s-most-connected-man-Vs~LzkbkR7yhjza~7nji1g.html
Meet theWorld's Most Connected Man
@pieroleo
Rapid growth of exogenous data is transforming healthcare
6 Terabytes
60%Exogenous Factors
1100 TerabytesVolume, Variety, Velocity, Veracity:Educational records, Employment Status, Social Security Accounts, Mental Health Records, Caseworker Files, Fitbits, Home Monitoring Systems, and more…
0.4 TerabytesElectronic Medical / Health Records, Physician Management Systems, Claims Systems and more…
30%Genomics Factors
10%Clinical Factors
IBM Watson Health // SOURCE: ©2015 J.M. McGinnis et al., “The Case for More Active Policy Attention to Health Promotion,” Health Affairs 21, no. 2 (2002):78–93
Data Generated per Life
@pieroleowww.pieroleo.com
@pieroleo
Leveraging Exogenous Data for Chronic Care (Type 2 Diabetes; Primary & Secondary Prevention)
60%Exogenous Factors
30%Genomics Factors
10%Clinical Factors
IBM Watson Health // SOURCE: ©2015 J.M. McGinnis et al., “The Case for More Active Policy Attention to Health Promotion,” Health Affairs 21, no. 2 (2002):78–93
Glucose Monitoring
CalorieIntake
StressLevelsPhysical Activity
Other vital signs SocialInteraction
Affinity (retail)
Sleep Pattern
@pieroleowww.pieroleo.com
@pieroleo
16
>80% Unstructured Data
+ External Data“Untouched” Data+ Stream of Data
Enterprise Data Machine Data People Data@pieroleo
www.pieroleo.com
@pieroleo
Source: http://datacoup..com
Value of Data
Pietro Leo's SecondIncome!
@pieroleowww.pieroleo.com
@pieroleo
For Science, Big Data is the microscope of the 21st century
Wine DNA Tracing
@pieroleowww.pieroleo.com
@pieroleo
Source: Cornell University - Maize kernal infected with Aspergillus flavus, which producedaflatoxin.http://www.plantpath.cornell.edu/labs/milgroom/Research_aflatoxin.html And http://www.special-clean.com/special-clean/en/mold/mold-lexicon-1.php
For science, Big Data is the microscope of the 21st century
@pieroleowww.pieroleo.com
@pieroleo
Source: http://www.pnas.org/content/114/38/10166.full
New kind of prediction microscope: With the DNA raw data you could predict some individual traits
@pieroleo
ALGORITHMS PROBLEMSDATACOMPUTING
• QUANTUM LAWS
• DIGITAL
• ANALOG
High Complexity
• WISDOM
• KNOWLEDGE
• INFORMATION
• NUMBERS
• TALK WITH A DIGITAL HUMAN
• DGITAL TRANSFORMATON
• ASK FOR A LOAN
• 2+2=4
• MACHINE LEARNS WITHOUT A TRAINING
• SUPERVISE AND TRAIN A MACHINE
• PROGRAM
LowComplexity
Dimensions of IT systems and their complexity
@pieroleowww.pieroleo.com
Credits: http://www.arkive.org/whale-shark/rhincodon-typus/
What isFintech?
27@pieroleo
www.pieroleo.com
« UNBUNDLING » is general phenomena that is impacting every sectors or corporations
Source: https://www.cbinsights.com/blog/smart-home-market-map-company-list/
SMART HOMES
@pieroleowww.pieroleo.com
« UNBUNDLING » logistics
Source: https://www.cbinsights.com/blog/startups-unbundling-fedex/@pieroleo
www.pieroleo.com
35
You shared your position with me and can guess your mobility need. I can take you where you need to be
Just enjoy your new experience. Stay safe as in your home
I know what is needed for you, even before you order it
Please, come with me and stay by me.I know your content I can take care of all your digital life
@pieroleowww.pieroleo.com
Automation will bring big shifts to the world of work, as AI and robotics change or replace some jobs, while others are created.
Source: McKinsey & co: Jobs Lost, Jobs created: workforce transactions in a time of automation, 2017
By 2030, 75 million to 375 million workers will need to switch occupational categories.
60% percent of occupations have at least 30% of constituent work activities that could be automated
@pieroleowww.pieroleo.com
Leveraging the Explosion of Data in Medicine An Impossible Task Without Analytics and New advanced Artificial Intelligence Computing Models
1000
Factsp
erDecision
10
100
1990 2000 2010 2020
HumanCognitiveCapacity
ElectronicHealthRecords(ClinicalData)
InternetofThings(Exogenous Data)
TheHumanGenome(GenomicData)
CapturingtheValueofData:BigChangesAhead
Medical error—the third leading cause of death in the US
Source:BMJ 2016; 353 doi: http://dx.doi.org/10.1136/bmj.i2139 (Published 03 May 2016) Cite this as: BMJ 2016;353:i2139
38
Body Mass Index (BMI)
Mass (weight - Kg) / height (cm) x height (cm)
You are “Normal” if your BMI is between18.5 and 24.99
Adolphe Quetelet, 1832@pieroleo
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39
Practice Pearls:• BMI - Body mass index is a strong and independent risk factor for being diagnosed with type 2 diabetes mellitus• Type 2 diabetes risk may be incrementally higher in those with a higher body mass index• Understanding the risk factors helps to shorten the time to diagnosis and treatment
How precise could be a “simple” signal
@pieroleowww.pieroleo.com
Assistant
Tools
Collaborator
Coach
Mediator
Emerging types of Cognitive Systems
Augment Decision Making is opening to new forms of collaboration between humans and machines
to solve problems
@pieroleowww.pieroleo.com
Assistant
Tools
Collaborator
Coach
Mediator
Augment Decision Making is opening to new forms of collaboration between humans and machines
Emerging types of Cognitive Systems @pieroleowww.pieroleo.com
Assistant
Tools
Collaborator
Coach
Mediator
Augment Decision Making is opening to new forms of collaboration between humans and machines
Emerging types of Cognitive Systems @pieroleowww.pieroleo.com
@pieroleo
2011 2015
2016 - AlphaGO=4 Lee Se-Dol=1
1997 - IBM=2.5 Kasparov=2.5
1997
AlphaGO uses self-trained net to evaluate positions and moves on 30M historical games
DeepBlue uses a hard-coded objective function written by a human coupled with High Performance Computing
2016
10
10170
1040
Applying or having wisdom in real world is not only an AI game
COMPUTING & MATH WISDOM
IBM Watson – Jeopardy!
SEMANTICS
Watson OncologyA collaboration between IBM and Memorial Sloan Kettering (MSK). Watson for Oncology utilizes MSK curated literature and rationales, as well as over 290 medical journals, over 200 textbooks, and 12 million pages of text to support decisions.
• Analyzes the patient's medical record• Identifies potential evidence-backed treatment options• Finds and provides supporting evidence from a wide variety of sources
46
TheMedicalSieve § Build a fast anomaly detection engine
– Quickly filters irrelevant images– Highlights disease-depicting regions– Flags coincidental diagnosis
§ Intended as a radiology assistant – Clinicians still do the diagnosis– Machine reduces workload – Machine performs triage/decision
support
Given history of the patient and images of a study
Is there an anomalous image here?If so, where is the anomaly ?Describe the anomaly
TheMedicalSieve
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@pieroleowww.pieroleo.com
https://www.thenorthface.com/xps
I am going to New York next May
Man
Walking, go around
vest
I'll find a jacket that fits those conditions. Are you looking for a men's or women's jacket?
Okay, I got it. What will you use this jacket for?
Where and When will you be using this jacket?
What styles are you looking for?
@pieroleowww.pieroleo.com
I am going to New York next May
Man
Walking, go around
vest
Where and When will you be using
this jacket?
I'll find a jacket that fits those conditions. Are you
looking for a men's or women's jacket?
Okay, I got it. What will you use this jacket
for?
What styles are you looking for?
@pieroleowww.pieroleo.com
@pieroleowww.pieroleo.com
Conversation Intents Entities Dialogues
Personality 5Five Personality Traits Needs Values
Language Tone Emotion Social propensities Language styles
Translate Conversational News Custom TranslationPatents
Language DeepUnderstanding
Speech-to-text
Custom pronunciations Voice Transformation
Expressive Voice
Voice synthesis
Keyword Spotting Telephony Broadband
Vision Face Recognition Image Similarity Image ClassificationCustom eyes
Keyword Extraction, Entity Extraction, Sentiment Analysis, Concept Tagging,
Relation Extraction, Taxonomy Classification, Author Extraction….. Custom Analysis
WATSONKind of skills
Source: https://www.ibm.com/watson/developercloud/services-catalog.html @pieroleowww.pieroleo.com
@pieroleo
Watson Chef with Bon Appétit
Live at: https://www.ibmchefwatson.com/tupler @pieroleowww.pieroleo.com
Assistant
Tools
CollaboratorCoach
Mediator
Augment Decision Making is opening to new forms of collaboration between humans and machines
Emerging types of Cognitive Systems @pieroleowww.pieroleo.com
53
2 solutions that address the biggest needs in Diabetes Care
Integrated & personalized diabetes care program with coaching services &
risk stratification for healthcare systems to help high risk/at-risk
individuals with diabetes improve their lives & reduce the cost of care
Personalized diabetes mobile companion with real time
glucose insights for individuals with diabetes to help make daily diabetes management easier and more effective
MedtronicTurning PointBridging the gap
in between doctor visits enabling
Self-Management & Better Care
53© 2016 International Business Machines Corporation@pieroleo
www.pieroleo.com
Sugar.IQ : Intelligent Mobile Assistant
Guardian Connect Cognitive Computing Sugar.IQ
• Real-time, smartphone CGM with alerts
• Insulin, Meal, Activity, Context• Standalone, for MDI patients
• Watson Health Cloud & Analytics• Pattern recognition, insights &
Predictions• Engagement & Gamification
• Real-time insights, coaching platform
• Assists in daily diabetes mgmt• Aggressive capabilities
roadmap
Insights GlucoseForecasts
Hype-hyperPredictions Ask
Watson
@pieroleowww.pieroleo.com
Assistant
Tools
Collaborator
Coach
Mediator
Types of Cognitive Systems
Augment Decision Making is opening to new forms of collaboration between humans and machines
@pieroleowww.pieroleo.com
ViTA Advisor: it is a conversational multi-modal agent to support older as well as a tool to collect meaningful data about the context of an individual
ViTA : Virtual Trainer for cognitive impaired patients
Sustain Independence and Dignity with affect and purpose, preserve and reinforce individuals and social memories
Vita Memory Coach: a system that supports caregivers to collect meaningful facts and memories of an individual and his context
Vita
Memories
@pieroleowww.pieroleo.com
Assistant
Tools
Collaborator
Coach
Mediator
Augment Decision Making is opening to new forms of collaboration between humans and machines
Emerging types of Cognitive Systems @pieroleowww.pieroleo.com
Assistant
Tools
Collaborator
Coach
Mediator
Augment Decision Making is opening to new forms of collaboration between humans and machines
Emerging types of Cognitive Systems @pieroleowww.pieroleo.com
64@pieroleo
www.pieroleo.comSource: Soul Machine - https://www.youtube.com/watch?v=0tUSsqOLZC8 @pieroleowww.pieroleo.com
Sphie
Source Sophie Soule Machine - https://www.youtube.com/watch?v=JiYANsW2F5A @pieroleowww.pieroleo.com
@pieroleo
ALGORITHMS PROBLEMSDATACOMPUTING
• QUANTUM LAWS
• DIGITAL
• ANALOG
High Complexity
• WISDOM
• KNOWLEDGE
• INFORMATION
• NUMBERS
• TALK WITH A DIGITAL HUMAN
• DGITAL TRANSFORMATON
• ASK FOR A LOAN
• 2+2=4
• MACHINE LEARNS WITHOUT A TRAINING
• SUPERVISE AND TRAIN A MACHINE
• PROGRAM
LowComplexity
Dimensions of IT systems and their complexity
@pieroleowww.pieroleo.com
@pieroleo
67
There is a constant growing interestaround Artificial Intelligence
@pieroleowww.pieroleo.com
@pieroleo
69
AI system & sighting
General Purpose Visual Services
Source IBM Research Computer Vision: http://www.research.ibm.com/cognitive-computing/computer-vision/
Medical Image Analysis
“a person holding a giraffe in their hand”
Video Content Analysis Image Captioning Low-power computer vision - Gesture Recognition
Multimodal Analysis
@pieroleowww.pieroleo.com
@pieroleo
70
Source: IBM Research automatic sport highlights generation https://www.ibm.com/blogs/research/2017/06/scaling-wimbledons-video-production-highlight-reels-ai-technology/ @pieroleo
www.pieroleo.com
@pieroleo
71
Source: IBM Research Food Recognition - https://www.ibm.com/blogs/research/2017/05/training-watson-see-whats-plate @pieroleowww.pieroleo.com
@pieroleo
72Source: IBM Research Image Caption generation paper - https://arxiv.org/pdf/1612.00563.pdf
“a blue boat is sitting on the side of a building” “a person holding a giraffe in their hand”
@pieroleowww.pieroleo.com
@pieroleo
74
Perception
Deep Reason
Classification
Explain
InterpretabilitySymbolicReasoning
Observe
Common-Sense
PlanningPatterns & Sub-patternsObservation
AI Algorithms
….. ….. …..
Ethics
@pieroleowww.pieroleo.com
@pieroleo
75
Perception
Deep Reason
Classification
Explain
InterpretabilitySymbolicReasoning
Observe
Common-Sense
PlanningPatterns & Sub-patternsObservation
AI Algorithms
….. ….. …..
EthicsDeep Neural Learning
@pieroleowww.pieroleo.com
@pieroleo
76
Deep learning basic processForward Propagation
Backward Propagation
Multiply + Add
SigmoidSoftMaxreLu
Multiply + Add
Errors
@pieroleowww.pieroleo.com
@pieroleo
78
Perception
Deep Reason
Classification
Explain
InterpretabilitySymbolicReasoning
Observe
Common-Sense
PlanningPatterns & Sub-patternsObservation
AI Algorithms
….. ….. …..
Ethics
See: https://www.ibm.com/watson/advantage-reports/future-of-artificial-intelligence/ai-innovation-equation.html@pieroleo
www.pieroleo.com
@pieroleo
79Source: https://www.ibm.com/watson/products-services/
ConversationIntegrate diverse conversation technology into your application.
KnowledgeGet insights through accelerated data optimization capabilities.
VisionIdentify and tag content then analyze and extract detailed information found in an image.
SpeechConvert text and speech with the ability to customize models.
LanguageAnalyze text and extract meta-data from unstructured content.
EmpathyUnderstand tone, personality, and emotional state.
Practical exercise: explore how an AI set of basic services looks like
@pieroleowww.pieroleo.com
@pieroleo
ALGORITHMS PROBLEMSDATACOMPUTING
• QUANTUM LAWS
• DIGITAL
• ANALOG
High Complexity
• WISDOM
• KNOWLEDGE
• INFORMATION
• NUMBERS
• TALK WITH A DIGITAL HUMAN
• DGITAL TRANSFORMATON
• ASK FOR A LOAN
• 2+2=4
• MACHINE LEARNS WITHOUT A TRAINING
• SUPERVISE AND TRAIN A MACHINE
• PROGRAM
LowComplexity
Dimensions of IT systems and their complexity
@pieroleowww.pieroleo.com
@pieroleo
“Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical, and
by golly, it’s a wonderful problem, because it doesn’t look so easy.”
-RichardP.Feynman
NATURE ISN’T CLASSICAL, DAMMIT, AND IF YOU WANT TO MAKE A SIMULATION OF NATURE, YOU’D BETTER MAKE IT QUANTUM MECHANICAL, AND BY GOLLY, IT’S A WONDERFUL PROBLEM, BECAUSE IT DOESN’T LOOK SO EASY.”
RICHARD P. FEYNMAN
“
QuantumAlgorithmsA quantum computer can solve certain problems much faster.
Deck with99 black cards
1 red Ace Problem:find red Ace
Average-case scenario:
Classical Algorithm: 51 card draws
Grover Quantum Algorithm: 10 card draws
@pieroleowww.pieroleo.com
QuantumAlgorithms
A quantum computer can solve certain problems much faster.
Deck #1100 black cards
Deck #250 black cards
50 red cards
Problem:which deck?
Worst-case scenario:
Classical Algorithm: 51 card draws
Deutsch-JozsaQuantum Algorithm: 1 card draw
@pieroleowww.pieroleo.com
Exponentialspeed-up:Atasktaking300years(233 seconds)onaclassicalcomputermighttakeaminute(~ 30seconds)onaquantumcomputer
Theproblemofmultiplicationvsfactoring:937x947=N(easy)887339=pxq(harder)
Modulus(1024bits):deb72643a69985cd38a71509b9cf0fc9c3558c88ee8c8d2827244b2a5ea0d816fa61184bcf6d6080d335403272c08f12d8e54e8fb9b2f6d9155e5a8631a3ba86aa6bc8d9718ccccd27131e9d425d38f6a7aceffa62f31881d424467f01777cc62a891499bb98391da819fb3900447d1b946a782d69adc07a2cfad0da201298d3
1024bitpublickey:
=p× q
→ justshortofimpossible Shor’s algorithmjumpstartedtheinterestinquantumcomputing
ClassicalRecord:320digits(𝟐𝟏𝟎𝟔𝟏;>300CPUyears)
exp(𝐶𝑏./0)
𝐶𝑏0
QuantumAlgorithms
@pieroleowww.pieroleo.com
@pieroleo
TravelingSalesmanProblem
- Visitallcitiesjustonce- Choosetheshortestpath- Comebacktostartingpoint
17x… x5x4x3x2x1=17!=355’687’428’096’000 possiblepaths
A quantumcomputercanexploreallroutessimultaneouslywhileaclassicalcomputerhastotrythemsequentially. 18selectedcitiesinSwitzerland
16ausgewählte Städte inderSchweiz
@pieroleowww.pieroleo.com
@pieroleo
MolecularDynamics,DrugDesign&MaterialsSimulationChallenges
This laptop could simulate a 25 electron system, Titan a 43 electron system but no classical computer ever built could simulate a 50 electron system exactly.
Caffeine is a moderately sized molecule. It is more complex than water, but simpler than DNA or proteins.The complexity involved to simulate its energy, structure and interactions is beyond the capability of any computer with current technology.To simulate Caffeine on Quantum might take 160 Qubits, doing so with classical with current methods require around 10^48 bits (as there are atoms on planet Earth)
@pieroleo
Some of the earliest examples of these include optimization, chemistry, and machine learning.
Quantum Computing holds the potential to solve today’s intractable computational challenges
@pieroleo
How quantum mechanics laws (such as superposition, entaglement, interference, etc..) could help to build new computational methods to solve these problems more efficiently?
@pieroleowww.pieroleo.com
@pieroleo
0
1
QuantumQUBIT
‘1’
‘0’
‘0’ + ‘1’
ClassicBITS
Superposition:eachqubit in2statessimultaneously
How Quantum laws can speedup Information representation?
@pieroleowww.pieroleo.com
@pieroleo
50 qubits
20 qubits
https://www.ibm.com/blogs/research/2017/11/the-future-is-quantum/https://www-03.ibm.com/press/us/en/pressrelease/53374.wss
@pieroleowww.pieroleo.com
@pieroleo Whereas a 56-qubit quantum computer can theoretically perform 2^56 operations simultaneously, IBM's accomplishmentinvolved dividing this task into 2^19 slices that each essentially consisted of 2^36 operations. This strategy meant the researchers only needed about 3 terabytes of memory for their simulated quantum computer.
In contrast, earlier in 2017, a 45-qubit simulation at the Swiss Federal Institute of Technology in Zurich required 500 terabytes of memory. @pieroleo
www.pieroleo.com
@pieroleo State-of-the-artquantumcomputinginnovationcontinuesatIBM
Research
Published in the journal Nature in September, 2017
@pieroleowww.pieroleo.com
Source: https://www.theguardian.com/technology/2016/sep/08/artificial-intelligence-beauty-contest-doesnt-like-black-people@pieroleo
www.pieroleo.com
@pieroleo
Source: http://www.ted.com/talks/sherry_turkle_alone_together
Sherry Turkle:Connected, but alone?
These days phones in our pockets are changing ourminds and hearts offer us three gratifying fantasiesand NEW challenges and risks for us:
1) We can put our attention where we want to be
2) We always be heard
3) We never left to be alone
@pieroleo
100
Source: https://www.ibm.com/blogs/think/2017/01/ibm-cognitive-principles/
Source: https://www.research.ibm.com/software/IBMResearch/multimedia/AIEthics_Whitepaper.pdf@pieroleo
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