Learning Startup to Watch: Declara’s Vision of a Cognitive Graph

Declara is one of the most unique startups in the world of enterprise-scale learning platforms.  The company has built an intelligent social learning system that is often referred to in the media as a combination of Google’s Knowledge Graph and Facebook’s Social Graph.

Declara’s vision is to create and leverage a Cognitive Graph that delivers neuroscience-inspired personalized learning based on the context of real-world experiences, intent, outcomes and social relationships.

The system aims to deliver content recommendations and facilitate the most appropriate social connections between learners across large organizations and social communities. The platform integrates the latest capabilities of artificial intelligence subdomains – machine-learning and  deep-learning to scale-up predictive analytics and prescriptive learning experiences based on an individual’s intentions, capabilities and needs.

The company sees a very rich and untapped landscape of learning analytics that will benefit from neuroscience-based insights on learning experiences.  The ‘adaptive’ and ‘intelligent’ labels simply mean that Declara’s infrastructure learns over time based on real-world interactions and outcomes.

Declara’s CEO Ramona  Pierson (Twitter) has an amazing comeback life story and a brilliant mind that sees the convergence of neuro-cognitive science, intelligent social systems, semantic search, graph databases, et al. Co-Founder Nelson Gonzalez (LinkedIn; Twitter) brings a pragmatic and optimistic lens to learning analytics and the intersection of local cultural elements and semantic search.

Declara has a very clear scale-out oriented business model that targets large customers such as national government associations (e.g. Mexico’s SNTE, Australia’s CSE) and enterprises like Genetech. They picked a wonderful problem to solve. Declara is a startup to watch…!!

Learn More: 

 

Interesting links on cognitive graph:

Videos

Ramona Pierson

Interview at 2014 Gigaom event 

 

 

Ramona Pierson speaking

 

Nelson Gonzalez – 2010 brief interview – hopefully more Youtube clips will appear soon!

https://www.youtube.com/watch?v=xmjDVrz5X5g

From Apple’s Knowledge Navigator to Mindmeld: The Evolution of Personal Assistant

Expect Lab‘s Marsal Gavalda walks us through 26 minute video of techno-optimistic geek goodness by looking at present day enthusasiam for personal assistant technologies and some of the historical milestones that brought us here.

Why the enthusiasm in 2014?  The Spike Jonze’s movie Her gets credit for popularizing the idea of a likeable and lovable personal assistant but the real source of optimism is just old fashion innovation from our learning curve.  Artificial intelligence sub-domains of machine-learning and deep-learning (used for real-time understanding of natural language) are making steady progress. The past few years have given the world very positive advances around knowledge graphs for natural language, sentiment analysis of unstructured data, and anticipation oriented recommendation systems.

The next five years will bring hype and real hope for functional contextual search and conversation-based experiences that make personal assistant beyond 2020 likely and doable.  I have waxed poetic about Expected Labs MindmeldAPI and have the same respect for companies like NextIt and Artificial Solutions (Indigo) who are creating the early market demand.

My highlights from his talk: min 2:20 Github workflow and productivity visualization]

https://www.youtube.com/watch?v=fKen7IkdAm0

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Marsal mentioned the Apple 1987 video of the Knowledge Navigator

https://www.youtube.com/watch?v=QRH8eimU_20

 

 

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Garry’s Tags: Personal Assistant;

 

 

 

Cognition-as-a-Service: Rent Learning Machines Inside the Cloud

sean gourley scereenSummary: Siri and IBM Watson for everyone? Organizations might soon be able to rent brain power by the hour from the cloud – creating a new marketplace for contextually-aware and adaptive applications powered by software programs that learn from real-world human user interactions.  Our educational and workflow experiences might soon access cogntive computing applications to learn more effectively and make better decisions on complex issues.

Companies are starting to experiment with business models to deliver cloud based cognition in the form of ‘contextual and cognitive computing’ via API Engines and stand alone software solutions.

Contextual & Cognitive Computing
Cognition as a Service is just a buzzword today.  It is far from defined! The most likely path towards bringing cognition level abilities to organizations will likely pass through stages.  The first will be an evolution towards contextual experiences followed by more sophisticated ‘IBM Watson’-like applications.

Contextual web experiences move us from information being delivered by ‘keyword’ connections to a more personalized sense of the right ‘context’ for our lives based on: location, activity, life experiences and preferences, et al.  Contextual experiences are more personalized and certainly ‘learn’ from interactions with users – but there is a new paradigm of ‘cognitive’ systems that elevate the experience.

Cognitive Computing is an era where software systems learn on their own and can teach themselves how to improve their performance.  IBM Watson is today’s most sophisticated cognitive application.  Watson is currently providing decision support for cancer treatments at Sloan Kettering, financial service support for companies such as ANZ and CitiGroup, and working to evolve the retail customer experience for Northface.

Cognition as a Service?
In practical terms this means an organization might be able to buy ‘as a service’ (e.g. not an application they paid to develop or maintain) – natural language processing for human-like Question & Answer interactions.

Companies pushing both of these capabilities are simultaneously trying to improve performance, integration and business model design.  Early industry adopters will range from health, finance, energy, education, et al.  Industries with connected data and a need for augmenting human knowledge building. Early adopters industries will likely have lots of data and compliance heavy regulatory frameworks.

 

The cloud-based business models of ‘software-as-service’ and ‘platform/infrastructure-as-service’ are the most likely first path for contextual and cognitive platforms.  Startups such as Expected Labs are bringing their API engine MindmeldAPI into the marketplace.  IBM Watson is hedging its bets by delivering stand alone solutions and also opening up its API to developers.

Developers are just now getting their hands dirty with advanced contextual computing applications. Truly transformational applications will likely emerge 2015-2025.

Companies to Watch:
AlchemyAPI, DeclaraIntelligent ArtificatsGrokSaffron, Stremor PlexiNLP  and Vicarious are companies with products designed to build knowledge graphs and natural language interactions that have contextual and cognitive computing style capabilities.

Video to Watch – Range of Business Models? 

Sean Gourley – Founder of Quid has been wrapping his head around the future of mankind working ‘with’ machines for several years.  I’ve seen Sean’s talk evolve over two years– and it continues to be among the most solid framings of this massive transition towards ‘augmented intelligence.

This is a great talk by Sean Gourley from the March 2014 GigaOm Data Structure Conference.

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I’ve posted Expected Labs Mindmeld videos here

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Looking for something more mainstream and business oriented:

IBM Waston Playlist here 

 

 

 

Garry’s Diigo Tags on: IBM Watson;

 

OYOD Own Your Own Data Movement

Summary: What type of data will make people want to protect it?  Forget about social web data, personal health and lifelong learning data and analytics offer the most compelling niches to raise public awareness to own and control our personal data.  Health data goes beyond clinical electronic health records (EHR) to include lifestyle analytics currently championed by the quantified self movement. Learning data goes far beyond high stakes test scores to include life-enriching experiences captured via the ExperienceAPI (TinCan) standard and controlled by the learner.

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Connected Data innovations for Health-Wellness & Lifelong Learning shape an experience industry 

Where might the Own Your Own Data (OYOD) movement find its momentum?
The vision of an Own Your Own Data (OYOD) future is a world of informed and empowered individuals who can control their own personal data and leverage (via opt-in) it with companies, organizations and governments as they see fit.

Elevating data literacy and social norms on how to control, protect and apply our own personal data will take years to unfold.  As of early 2014, the ‘OYOD movement’ is dispersed and off the radar.   By the end of the year things could be very different. Projects such as IrisPact (pronounced I RESPECT) and media attention on personal data could shift expectations and set the stage for OYOD policies to be implemented.

Where might OYOD gain momentum? The near term target is tilting the balance of power over our social web data back to the user. Protecting personal data and advocating for ownership within social web environments is a worthy goal but late in the game to try and change the rules.  Most existing social data projects are shallow efforts essentially linked to controlling our own precision advertising profiles.

If we are looking for arenas that an Own Your Own Data (OYOD) movement could emerge– healthcare and lifelong learning are two possibilities.

Health
The idea pushed within the healthcare and wellness space is broadly known as Personal Health Records or Electronic Health Records (EHR). These platforms allow individuals to gather, protect, share and synthesize individual and family health data records.  It is an important transition but insufficient in understanding health-wellness issues beyond clinical setting.  Lifestyle health analytics currently found within platforms and APIs from the quantified self community could compliment EHR records to give a more complete real-world picture.

Imagining a world with ‘ownership’ of personal health data is a complicated futures scenario, but plausible and certainly powerful enough to build popular support for ‘OYOD’ policies.

Health Data Projects to Watch:
BlueButton (US); OpenHealth Data (UK); 
SMARTPlatforms; DossiaPatients Know Best; MyPHR; Indivio; Open EPIC; Kaiser Permanente Interchange, Atena CarePass, et al.

Learning
In recent years we built our ‘social graph’ that outlines who we know and how we know people by relationships.  In the next decade many of us will build our own ‘learning graph’ of what we know and how we know concepts across a wide range of domains.

Building a data-driven ecosystem for controlling our learning graphs is complicated. It is never wise to try and place bets on data standards – but I am bullish on the long-term impact of two enabling foundations to record and leverage lifelong learning experience data.

The first concept to watch is: ExperienceAPI (or TinCanAPI) the next generation (post SCORM) standard application protocol of ‘activity statements’ (I did this…) that allow us to choose when we capture learning experiences. Learning activity statements can be online or offline – within school, work settings or walking in a park. (e.g. I read x-book. I attended x-workshop. I wrote y-book. I earned a masters degree from x-university.  I watched x-TED talk. I visited x-museum exhibit. I took photographs of x-flowers. I read a NYTimes article on x-topic).

These ExperienceAPI statements are stored in a LRS (Learning Records Store) platform that gives individuals control over which “I did this…” life experience statements can be shared with other people, institutions or companies.  Access to specific LRS data streams allows organizations to dynamically adjust information and experiences to individuals.

There are significant barriers to imagining an OYOD world of lifelong learning but there are paths forward which I will explore in future blog posts.

Learning Data Project to Watch:
ExperienceAPI (TinCanAPI), WatershedLRS, SaltboxWAX LRS;
Knowledge Graphs; Adaptive Learning Platforms  

 

There are other angles to Lifelong Learning data.  Adaptive learning platforms; and Danny Hillis’ vision of a Learning Graph

 

Image Use: Creative Commons URL

Startup to Watch: Expected Labs & Mindmeld Intelligent Assistant

Some of the world’s most visionary and innovative software teams are working to develop intelligent assistant applications that stand at the crossroads of knowledge graphs, natural language processing and context aware user experience design.

Consumer grade intelligent assistant products include Apple Siri and Google NowThe business world is watching IBM Watson – a Deep Q&A service based on Big Blue‘s cognitive computing era vision for business applications in healthcare, finance and customer service.

These large software technology companies are not alone. There are dozens of intelligent assistant startups helping to drive the field forward.  Which startups are worth following?

Startups to Watch: Expected Lab (Mindmeld) 
Expected Labs first received attention in 2012 when its Mindmeld application won over hearts and minds as Finalists at TechCrunch Disrupt.  In 2013 the company secured more funding to bring Mindmeld to market.

http://www.youtube.com/watch?v=5NGGSBt0hkw

Expected Labs is a young company working on an old problem. The idea of computer as an intelligent personal assistant is as old as the history of computing machines.  The most contemporary lineage dates back to the U.S. DARPA sponsored the CALO program (Video of Cognitive Assistant that Learns & Organizes).  CALO’s platform was spun-off into technology development firm SRI  – which in turn sold a platform to Apple for its Siri applications.

The intelligent assistant product landscape has seen significant advances in recent years thanks to advances in machine-learning, deep-learning, data graphs, semantic collection volumes — and more transparent and predictable human users.  The next five years will be an exciting time for the community ;-).   

Expected Labs is a team to watch in 2014 and beyond.

Videos below on Mindmeld and the future of intelligent assistants

Product Promotion

http://www.youtube.com/watch?v=5NGGSBt0hkw

 

Updated: Detailed Product Overview

http://www.youtube.com/watch?v=XKGcvRkagmY

 

 

Expected Labs CEO Tim Tuttle (Twitter) has created a series of short videos looking at the Future of Intelligent Assistants around key areas of performance improvement: Speed, Accuracy, Intelligence, and Anticipation. Tuttle is a big thinker with a sharp mind and sense of where knowledge graphs and anticipatory user experiences could evolve!

Let Tim Tuttle explain why we should be bullish on the age of intelligent assistants…

Speed

http://www.youtube.com/watch?v=L-PoDin69-g

Understanding the User

http://www.youtube.com/watch?v=vN266-vlmiQ

** I think ExperienceAPI and Learning Record Stores (LRS) could be integrated here!

Accuracy 

http://www.youtube.com/watch?v=irRKPlUDGNA

Role of Knowledge Graphs

http://www.youtube.com/watch?v=HciVAMkmeS0

Contextual Computing – Focus

http://www.youtube.com/watch?v=c2q31dMHX4E

 

 

Learn More?

Casual or skeptical observers might want to consider the future of Intelligent Assistants and how society might approach the idea of anticipatory computing experiences and the notion of the device listening to you!  These platforms will bring new risks, rewards and responsibilities to our digital culture.

More curious technical minds will want to learn about machine-learning, deep-learning, graph databases (Neo4j), semantic graphs (e.g. ConceptNet), TinCan-ExperienceAPI, Learning Record Stores (LRS), et al.

Garry delivers keynotes, workshops and consultation for organizations around the world! Lets talk about how he can help yours.