iacsf: What’s Next? Shaping the Future

Mark Yahiro, Intel
Timothy Tuttle, Expect Labs
Liesl Capper, IBM
Roberto Pieraccini, Jibo

Intel offers platform and foundation to use speech, motion, who you are, to use data in intelligent ways.

Jibo (a male character) is not humanoid, but has stereo camera, mic, speaker identification, motion or facial expression detection, display, touch points. Just got big crowned sourced funds. Japanese trend is to more humanoid, is creepy, uncanny valley. farthing can express feelings (teapot in beauty and beast).

Ongoing conversations? Watson uses experts systems, known data sets, to develop ranked diagnosis of medical conditions.

Movie Her and anticipatory agents? Tim suggests we’re not as far off as we think. We are going to starts seeing intelligence in new devices. Recent breakthroughs from IBM in deep learning remarkably reduce error rate within a couple of years.

What is the future? Cognitive glue working across other agents. Holy grail is all human interaction. There are already a lot of agents, interacting between and across them is learning a new language. Filter the right data specific usage, depending on the usage do we filter before or after? Where is context? What makes sense.

Speech recognition is about 60, 65 years old. Big problem is that once you understand how to did it, depends on the language and what you are talking about. Can’t create a closed loop.


iacsf: Michael Bowers, Coca Cola

Offering perspectives as an operator. They used early virtual agent, not effective. Thecoca-cola company.com and mycokerewards.com

Spikes in volume, can’t economically staff for this. How they planned and executed new agents: planning: choose a provider, interaction with site owners and consumer care (avatars, content that is relevant). Execution: apply project management rigor, be ready to add and modify content based on user experience, steward results (data) to site stewards. Graphs show traffic. Mycokerewards is about points, 30,000 visitors vs 15000 at other site. Super Bowl ad traffic story with spike for reasons they didn’t anticipate. User-created ad on YouTube also impacted. For them, saves money. Product locator now on Sprite site.

No perfect, lessons learned when hackers found their way in.

Intelligent channeling: be available, be where consumers prefer, architect experience to drive customers thoughtfully. Learn from the IVA.

Why is virtual agent below the fold? Marketing decisions, also IVA is America-centric and company is global.


iacsf: Norman Winarsky and Bill Mark, SRI

SRI started work with Calo, funded through DARPA, probably largest AI project in 1953. Focus on learning in the wild, the way a real human personal assistant would learn. Still quite a frontier. Biggest challenge is user experience, when things in the interface change, he’s out of control. Wants something that does the right thing every time. Calo was most major project before Siri. People who worked on Siri came out of Calo team.

sRI is very financially pragmatic about what they fund, must be relatively certain. We start with a market problem, not technology.

How important was natural speech? Global conversations with telephone ecosystem, most people don’t talk to a phone for service but Bill thought that wasn’t a future predictor. Is rare that customer expresses their intent in first sentence. W are learning what we want as we talk. One Question might lead to another.

Tempo, a smart calendar, offers insights into your daily life, predictive intent. Destiny is a travel company, bought by Nokia. Quantum game about math. Open question do we need verticals or can we use one? Different kinds of personal assistants. On kind is general, other is really about me.

There will be millions of assistants, but two layers. APIs to talk to each other? Th hard part is how to have this multi-term part conversation between all. Won’t stay mobile device centered, internet of things will help with simple intents that happen all the time. Which decisions are contingent on emotional responses or other timely options? Language, interacting, is not the future that is learning and understanding. Mobile will be center of interactions related to where, when…

Genderization is also a complex issue. Real world: not homogeneous, will be a variety in personas.

Personalization in retail, for example, can recommend, but puts you in a category. Difference between, and we need both little data as well as big.

Eliza, from 1960s, tried to emulate a psychologist. People really engaged, some went on for hours. Touring test of thinking machines… Some people want a human interaction, some dont.

What technologies need to improve before it’s conversational? Deep knowledge, represents fundamental things that are known to us (e.g., bank account), also context for human interactions and how we remember things. The way we deal with this now is through verticals, where context and intents are more established. Across market verticals not possible today. Shallow vs deep.

Tooling and analytics are useful to people buying them. Problems with deep knowledge, structuring the representations, and how it all works together.


IACSF: Intelligent Assistants Conference

Twitter hashtags are #iacsf and #iac2014. Live blogging begins.

Dan Miller, Opus Research opens the event.

Intelligent assistants are natural language processing, artificial intelligence, machine learning, knowledge management, also user interface: automated speech, text input and output, more

Drivers include chat, proactive service, cost savings. Uses include faqs, complex care, brand support, sales and engagement.

Intelligent assistance is really tailored to digital natives, rapid recognition of intent, social and conversational, supports c2b, minimizes customer Involvement. More than a PVC: “cognition as a service”.

David Lloyd, CEO of IntelliResponse
Documenting the need for customer-driven digital self-service

They’ve been around for years, lots of customers, including banks.

Primary research on 1000 consumers about attitudes of self service, do they care. Most (67%) go first to company website, 10% go to social media first. Customer service engagement also started on company website at 58%, phone only 25%. What kind of relationship do you want? Most 59% want efficient transactional service. Personalized service at 24%. 11% want ato buy and say goodbye.

Customers want consistency across channels. 74%.

60% age 18-34 want mobile access. Story off David’s dad, in his 80s is engaged too, so be careful about stereotypes. Customers experience frustration in trying to get answers or accomplish things by companies moving customers to different channels.

Goals from 100 organizations: primary goals include deflect calls, lower cost, make agents more effective, reduce website abandonment. Companies not “eating own dogfood,” should go to own site and try to accomplish FAQ problems.

Retail: best online experience 68%. Other industries vary, some at far end of good. Customer portals tend to be FAQ-driven, site search, customer portal, contact form; status quo not working. Story about lousy customer experience with uber, who responded 24 hours later.

Foundation of any solution is customer intent. Not same as search or unlimited sea of answers. Examples of different questions that require same answer

Optus in Australia: 4million questions asked, 94% accurately answered. Went from 84% phone and email to 65% online, phone and email at 24%. Ask Ana for ANA airlines – grew to 50% by June 2013 using online in first six months.