A reading of ibm research innovations - for 2018 and ahead

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IBM activities to push forwardthe Future of the IT Technology

Executive Architect – IBM Italy CTO for Big Data Analytics & AI – Chief Scientist for Research & Business IBM Italy – IBM Academy of Technology Leadership Team www.pieroleo.com

IBM Research innovation engines …. around the world

http://research.ibm.com/

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The Future of Man & Machine Relation

The Future of Machine Intelligence

The Future of Transactions

The Future of Problem Solving

The Future of Cyber Security

5 Major Themes

A reading of IBM ResearchTechnologyHot Areas

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4. The Future of Problem Solving

5. The Future of Cyber Security

2. The Future ofMachine Intelligence

1. The Future of Man & MachineRelation

3. The Future of Transactions

• Cognitive cybersecurity

• Homomorphicencryption

• Lattice cryptography

• Blockchaineconomies

• AI+Blockchain• Crypto anchors

• Quantum computing

• Edge computing• Nanotechnology

• Foundation of AI• World's smallest

computer• World’s Smallest

Magnet• In-memory

computing

• Artificial Interactions – Soul Machine

• AI systems & reasoning –Debating

• AI systems & sighting

• AI systems & voice

Artificial Intelligence will integrate seamlessly into and enhance the workplace

AI will have enhanced reasoning abilities and will be widely distributed -helping us make decisions instantly

Blockchainswill do for trusted transactions what the Internet did for Information

New methods of computing are emerging to solve problems that classic machines cannot even attempt or consider more effective ways to solve a problem

Within next few years most of the today's cybersecurity methods and protocols will be breached

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Artificial Intelligence will integrate seamlessly into and enhance the workplace

The Future of Man & MachineRelation

Challenges

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The Future of Man & MachineRelation

• Artificial Interactions – Soul Machine • AI systems & reasoning – Debating• AI systems & sighting• AI systems & voice

What we are doing?

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Artificial Interactions – Soul Machine

Soul Machine’s Greg Cross Presentation https://www.facebook.com/bwnet.fans/videos/10154882117121837/

Soul Machines ‘digital humans’ have own unique personality and emotional intelligence.

• creating a realistic physiological model,

• the persona, and• the self-learning from each

human interaction.

The IBM Watson platform enables these digital humans to learn an organization's corpus of knowledge and Soul Machines’ human computing engine allows them to embody the core values of the organization they will represent.

Soul Machine Digital Humans: https://www.youtube.com/watch?time_continue=2&v=AzPs7GfOkew

Baby 5.0 - https://www.youtube.com/watch?v=yzFW4-dvFDA

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The human face of Artificial

Intelligence

Air New Zealand Project: https://www.ibm.com/blogs/ibm-anz/digital-humans/

Soul Machines used neural networks and

biological brain models to bring Sophie to life, powered by a cloud

based Human Computing Engine.

IBM Watson not only did Sophie undergo domain-

specific training, but other aspects such as her Kiwi

accent and facial expressions were also

tweaked to ensure a high level of realism.

9IBM Research Debating Technologies Home Page: http://www.research.ibm.com/haifa/dept/vst/debating.shtml

“Argumentation and debating represent primary intellectual activities of the human mind. People in all societies argue and debate, not only to convince othersof their own opinions but also in order to explore the differences between multiple perspectives and conceptualizations, and to learn from this exploration.” ACL Berlin2016

AI system & reasoning - debatingwww.pieroleo.com

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IBM Unraveling Language Patterns1. Opponents often argue that the open primary is

unconstitutional. [Open Primaries]

2. Prof. Smith suggested that affirmative action devalues the accomplishments of discriminated groups. [Affirmative Action]

3. The majority stated that the First Amendment does not guarantee the right to offend others. [Freedom of Speech]

IBM Research GRASP system, -http://www.research.ibm.com/haifa/dept/vst/grasp.shtmlPaper: http://aclweb.org/anthology/D17-1141 GRASP

[someone][argue/suggest/state][that][topic term][sentiment term].[noun][express][that][noun,topic][sentiment],

Language Patterns in

1-2-3

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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

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Source: IBM Research automatic sport highlights generation https://www.ibm.com/blogs/research/2017/06/scaling-wimbledons-video-production-highlight-reels-ai-technology/

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Source: IBM Research Food Recognition - https://www.ibm.com/blogs/research/2017/05/training-watson-see-whats-plate

14Source: 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”

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Source: PowerAI DDL - https://arxiv.org/pdf/1708.02188.pdf

Source: https://www.ibm.com/blogs/research/2017/08/distributed-deep-learning

IBM Research beat Facebook’s time by training the model in 50 minutes, versus the 1 hour Facebook took.

Using this software, IBM Research achieved a new image recognition accuracy of 33.8% for a neural network trained on a very large data set (7.5M images).

The previous record published by Microsoft demonstrated 29.8% accuracy.

Training time: IBM=7 hours, MS=240 hours

ImageNet – 14.197.122 images, 21.841 categories

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2,400 two-sided telephone conversationsamong 543 speakers (302 male, 241 female) from all areas of the United States.

120 unscripted 30-minute telephoneconversations between native speakers of English.

Most participants called family members or close friends.

Conversation among notknown individuals

SwitchBoard Corpus Call Home Corpus

AI systems & voice

Source: https://catalog.ldc.upenn.edu/ldc97s62 Source: https://catalog.ldc.upenn.edu/ldc97s42

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Source: IBM Research - https://www.ibm.com/blogs/research/2017/03/speech-recognition/

Speech recognition

“SWITCHBOARD” corpus “CallHome” corpus

2000: 31.4% (Cambridge University)

2015:14.1% (IBM)2016:12.2% (IBM) - Apr2016: 11.3% (Microsoft) – Oct2017: 11% (Microsoft) – Feb2017: 10.3% IBM (Mar)

Human Parity 6.8% (to 11.3%)

1992: 70%-80%

2000: 19.3% (Cambridge University)2014: 15% (IBM)2015: 8% (IBM)2016: 6,9% (IBM) - Apr2016: 5,9% (Microsoft) - Oct2017: 5.8% (Microsoft) – Feb2017 5.5% IBM (Mar)

Human Parity 5.1% ( to 5.9%)

ErrorRate

ErrorRate

25 years 25 years

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IBM Research Voice: http://www.research.ibm.com/haifa/dept/imt/ivvc.shtml

IBM Research Virtual Voice Creator Demo: https://ivva-tts.sl.haifa.il.ibm.com/

US patent application 15/594606. “Text-to-Speech Synthesis with Dynamically-Created Virtual Voices”. Filed on 14 May 2017.A. Sorin, S. Shechtman, and A. Rendel, “Semi Parametric Concatenative TTS with Instant Voice Modification Capabilities”, in Proc. Of INTERSPEECH 2017

The IBM Virtual Voice Creator takesText-To-Speech synthesis technologyto the next level, letting enterprisecustomers and users create uniquevoice personas on-demand in a fast and easy way.

The IBM Virtual Voice Creator letsautomatically create a voiceover for a multi-character game, animation or educational video, without the hassleof hiring voice actors and audio recording studios.

Synthetic speechwww.pieroleo.com

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AI will have enhanced reasoning abilities and will be widely distributed - helping us make decisions instantly


Challenges

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• Foundation of AI• World's smallest computer• World’s Smallest Magnet• In-memory computing

What we are doing?

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There is a constant growing interestaround Artificial Intelligence

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Various forms of AI works www.pieroleo.com

23Santiago Ramon y Cajal Camillo Golgi

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Data

Algorithms

Compute

Foundation of AI

CPU GPU

Deep Neural Networks

A twin-engine aircraft every 12hrGenerate 844TB of Data

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Data

Algorithms

Compute

Foundation of AI

CPU GPU


A twin-engine aircraft every 12hr Generate 844TB of Data

1 Atom =1 bit

= new acceleratingareas

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Currently, hard disk drives use about 100,000 atoms to store a

single bit.

The storage feat couldsomeday lead to the ability

to store all 35 million songsin the iTunes library on an

area the size of a credit card.

IBM Researchers StoreData on World’s Smallest

Magnet -- a Single Atom

Source: IBM Research - https://www-03.ibm.com/press/us/en/pressrelease/51787.wss

Magnetic bits lie at the heartof hard-disk drives, tape and

next-generation magneticmemory

Details on Nature paper:: http://www.nature.com/articles/nature21371

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Data

Algorithms

Compute

Foundation of AI

CPU GPU


Deep Reasoning(Transfer, Trust, Symbolic…)


1 Atom =1 bit


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Perception

Deep Reason

Classification

Explain

InterpretabilitySymbolicReasoning

Observe

Common-Sense

PlanningPatterns & Sub-patternsObservation

AI Algorithms

….. ….. …..

Ethics

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Perception

Deep Reason

Classification

Explain


Observe

Common-Sense


AI Algorithms

….. ….. …..

EthicsDeep Neural Learning

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30Source: https://arxiv.org/pdf/1710.08864.pdf

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Perception

Deep Reason

Classification

Explain


Observe

Common-Sense


AI Algorithms

….. ….. …..

Ethics

See: https://www.ibm.com/watson/advantage-reports/future-of-artificial-intelligence/ai-innovation-equation.html

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Source: https://www.ibm.com/blogs/think/2017/01/ibm-cognitive-principles/

Source: https://www.research.ibm.com/software/IBMResearch/multimedia/AIEthics_Whitepaper.pdf

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Perception

Deep Reason

Classification

Explain


Observe

Common-Sense


AI Algorithms

….. ….. …..

EthicsDeep Neural Learning

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Deep learning basic process

Forward Propagation

Backward Propagation

Multiply + Add

SigmoidSoftMaxreLu

Multiply + Add

Errors

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Data

Algorithms

Compute

Foundation of AI

CPU GPUApproximateComputing

In-MemoryComputation

SynapticComputing

5nm – 30B Transistors


Deep Reasoning(Transfer, Trust, Symbolic…)


1 Atom =1 bit


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5 nanometre chips are possible, more powerful, and not too far off in the future.

Compared with 10nm technology available in the market, a nanosheet-basedfive nm technology gives a 40% performance boost.

Significant boost to meeting the future demands of artificialintelligence systems, virtualreality and mobile devices.

The world's first five-nanometre silicon chip. A finger-nail sized chips with 30 billion transistors.

Source: IBM Research 30B transistors chip https://www.ibm.com/blogs/think/2017/06/5-nanometer-transistors/

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http://science.sciencemag.org/content/345/6197/668IBM Research Brain Chip - http://www.research.ibm.com/articles/brain-chip.shtml

In 2014, IBM presented 1M spiking-neuron chip with a scalable communication network and interface. The chip has 5.4 billion transistors, 4096 neuro-synaptic cores and 256 million configurable synapses.

Neuromorphic Computing – IBM True North

Source: Introduction - https://www.youtube.com/watch?time_continue=3&v=jqI0L44yFEo

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Source: IBM Research Gesture recognition at Low power devices - https://www.ibm.com/blogs/research/2017/07/brain-inspired-cvpr-2017/

Source Video: https://www.youtube.com/watch?v=g08IW-qRomM

Trained a spiking neural network to recognize 10 hand gestures in real-time at 96.5 percent accuracy within a tenth of a second from the start of each gesture, while consuming under 200 mW – much lower power than frame-based systems, which use traditional processors.

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• 64 millionneurons

• 16 billionsynapses,

• Processor component willconsume the energy equivalentof a dim light bulb– a mere 10 wattsto power.

Source: IBM Research new TrueNorth project update https://www-03.ibm.com/press/us/en/pressrelease/52657.wss

U.S. Air Force Research Lab Taps IBM to Build Brain-Inspired AI Supercomputing System

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In-memory Computing with 1 Million Devices for Applications in AI

Source IBM Research Phase Change Memory: https://www.ibm.com/blogs/research/2017/10/ibm-scientists-demonstrate-memory-computing-1-million-devices-applications-ai/

IBM Researchdemonstrated that an unsupervised machine-learning algorithm, running on one millionphase change memory(PCM) devices, successfully foundtemporal correlations in unknown data streams.

When compared to state-of-the-art classical computers, this prototype technology is expected to yield 200x improvements in both speed and energy efficiency

Source: http://www.nature.com/articles/s41467-017-01481-9

PCM Device Collocated Memory and computing

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IBM and MIT to Pursue Joint Research in Artificial Intelligence, Establish New MIT–IBM Watson AI Lab

IBM plans to make a 10-Year, $240 MillionInvestment in new lab with MIT to advance AI hardware and software and algorithms

Advancing shared prosperity through AI

• AI & economic and societal benefits to a broader range of people, nations, and enterprises.

• AI & Ethics

AI algorithms

Developing advanced algorithms to expand capabilities in machine learning and reasoning.

Physics of AI

• AI hardware materials, devices, and architectures that will support future analog computational approaches to AI

• Quantum computing & Machine learning.

AI & industries

• Healthcare• Cyber Security

Source: http://mitibmwatsonailab.mit.edu/

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Blockchains will do for trusted transactions what the Internet did for Information


Challenges

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• Blockchain economies• AI+Blockchain• Crypto anchors

What we are doing?

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Welcome to the Blockchain economy!

Source IBM IBV Studies https://www-935.ibm.com/services/us/gbs/thoughtleadership/blockchainlibrary.html

2017

2016

Already Active in Blockchain

Investigating

Source: IBM IBV Surveyof 3,000 global C-suite executives

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Welcome to the Blockchain economy: the Charm effect

Already Active in Blockchain

Investigating

Source: https://en.wikipedia.org/wiki/Crossing_the_Chasm

Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers

According to Moore, the marketer should focus on one group of customers at a time, using each group as a base for marketing to the next group. The most difficult step is making the transition between visionaries (early adopters) and pragmatists (early majority). This is the chasm that he refers to. If a successful firm can create a bandwagon effect in which enough momentum builds, then the product becomes a de facto standard.

Chasm

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Blockchain & AI

Artificial Intelligence-driven analysis are

performed directly on the blockchain, cross-checking a myriad of

regulations and records as well as supply chain and IoT data, including time, date stamps and geospatial, as well as

visual information.

Source: IBM Research combines AI and Blockchain: https://www.ibm.com/blogs/research/2017/05/power-blockchain-watson/

Interconnect 2017 – Everledger Diamonds Tracking on Blockchaiins - https://www.youtube.com/watch?v=-zYnYXpmtoc

Advanced computer vision AI identifies a diamond from its natural properties, which can’t be changed

Automatic generation of smart contracts from the Kimberly Process certification scheme

40 metadata points

Everledger Diamonds

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Crypto Anchors

IBM Research crypto Anchors for Diagnostic devices: https://www.zurich.ibm.com/st/precision_diagnostics/cryptoanchors.html

Crypto anchors extend blockchain’sValue Into the physical realm. Tamper-proof signatures will authenticate products, from medicin to diamonds and make counterfeit nearly impossible

Crypto anchors come in many forms. They can even be embedded into an editable shade of a magnetic ink, which can be used to dye a malaria pill, for example

IBM Research Crypto Anchor for diagnostic devices

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• Quantum computing• Edge computing• Nanotechnology

What we are doing?

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New methods of computing are emerging to solve problems that classic machines cannot even attempt or consider more effective ways to solve a problem


Challenges

www.pieroleo.com

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The future is quantum

• A 20 qubits available on cloud to our clients at the end of this year (already made available 5-16 and 17 qubits)

• A prototype with 50 qubits has been successful tested with a double of coherence time with respect previous systems.

• An open source Quantum Information software (Qikit)

• Quantum Computing: Breaking Through the 49 Qubit Simulation Barrier

.Source: https://www.ibm.com/blogs/research/2017/11/the-future-is-quantumSource: https://www.ibm.com/blogs/research/2017/10/quantum-computing-barrier/

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Edge Computing - Mesh Networks

IBM Research Edge Computing - https://www.ibm.com/blogs/research/2017/02/bringing-edge-computing-to-life/Mesh Network Alerts from IBM and The Weather Company - https://www.youtube.com/watch?time_continue=5&v=IITqVRWvDAw

The idea behind edge computing is to process the data right where it isgenerated.

A stadium has potentially more rawcapacity than some of the most powerfulsupercomputers in the world when it’s full of twenty thousand people carryingsmartphones that sport a powerful CPU, array of sensors, storage and multiple radios for communications – and can be connected to one another.

Add tablets, drones, connected vehicles, and other smart devices into the mix, and you have beginnings of the next paradigmof computing.Available n 54 countries

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Separating bioparticles at the nanoscale for early disease detection - NanoDLD

New chip technology from IBM Research scientists could enable physicians to perform pre-symptomatic disease detection with a diagnostic device that separate interesting nanoscale-sized biological targets, readily available in saliva and urine.

.Access to bioparticicles such as DNA, viruses and exosomes

IBM Research NanoDLD device - https://www-03.ibm.com/press/us/en/pressrelease/50275.wss

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Within next few years most of the today's cybersecurity methods and protocols will be breached


Challenge

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• Cognitive cybersecurity• Homomorphic encryption• Lattice cryptography

What we are doing?

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Cognitive Threats Maps

• Gain local context leading to the incident

• Perform threat research and develop expertise

• Formulate a threat research strategy

• Apply intelligence to understand the threat

Sogeti Gets 50% FasterAnalysis Times

Source Identify and Understand threats with Watson for Cyber Security: https://www.youtube.com/watch?v=O4qftNyqiQA

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Source: G. Gentry, IBM Research, Computing Arbitrary Functions of Encrypted Data – ACM http://ece.gmu.edu/coursewebpages/ECE/ECE646/F10/project/F10_Project_Resources/Computing_Arbitrary_Functions_of_Encrypted_Data.pdf

Usable Full Homomorphic Encryption

2010 2011 2012 2013 2014

Estimated amortized timefor computing a single bitoperation on encrypted data

Moore’s law

2009-10: Plausibility[GH’11] A single bit operation takes 30 minutes

IBM Patent 2013

2011-2012: “Real Circuits”

[GHS’12] A 30,000-gate in 36 hours

2013-today: UsabilityHElib [HS’14]: IBM’s open-source FHE implementationBasis for “generic” FHE computation

An “assembly language” for FHEImplements Brakerski-Gentry-Vaikuntanathan (BGV) scheme

Security based on ring LWE (RLWE)The same 30,000-gate circuit in 4-15 minutes

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1. How to share genomic data in a way that preserves the privacy of the data donors, without undermining the utility of the data or impeding its convenient dissemination?

2. How to perform a LARGE--SCALE, PRVIACY--PRESERVING analysis on genomic data, in an untrusted cloud environment or across multiple users?

• Fast drop in the cost of genome--sequencing 2000: $3 billion

• Mar. 2014: $1,000 • Genotyping 1M variations: below

$200

Unleashing the potenDal of the technology• Healthcare: e.g., disease risk

detection, personalized• medicine • Biomedical research: e.g., • geno-phono• association• Legal and forensic• DTC: e.g., ancestry test, paternity

test

Full Homomorphic EncryptionExamples – Genome Analysis

Source: The 2nd Comparison on Critical Assessment of Data Privacy and Protect Secure Genome Analysis http://www.humangenomeprivacy.org/2015/slides/003_iDASH%20workshop%202015_setStage.pdf

Computing the Hamming distance between two Human Genomes under a Full Homomorphic Encryption schema

Source: https://arxiv.org/abs/1710.10377

https://arxiv.org/pdf/1512.04965.pdf

“One of our main findings is that the number of logical qubits required to implement a Grover attack on AES is relatively low, namely between around 3000 and 7000 logical qubits.”

“When realizing AES, only SubBytes Involves T-gates. Moreover, SubBytes is called a minimum of 296 times as in AES-128 and up to 420 times in AES-256. As shown above, for all three standardized key lengths, this results in quantum circuits of quite moderate complexity. So it seems prudent to move away from 128-bit keys when expecting the availability of at least a moderate size quantum computer.”

Source: https://www.iad.gov/iad/customcf/openAttachment.cfm?FilePath=/iad/library/ia-guidance/ia-solutions-for-classified/algorithm-guidance/assets/public/upload/CNSA-Suite-and-Quantum-Computing-FAQ.pdf

Commercial National Security Algorithm Suite

05 January 2016

Source: https://csrc.nist.gov/news/2016/public-key-post-quantum-cryptographic-algorithms

Source: IBM Researcher comments https://securityintelligence.com/preparing-next-era-computing-quantum-safe-cryptography/

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Assumptions for Quantum-Resilient Public-Key Cryptography

N=pqgx=y mod p

Factoring is hard

Finding short vectors in lattices is hard

Computing discrete logs is hard

(f,g) in Z[x]/(xn+1)

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Lattice-BasedCryptography

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IBM activities to push forwardthe Future of the IT Technology