At Rise of AI 2018 in Berlin, Ramin Assadollahi, CEO of ExB, argues that AI should be built as a cross-industry platform rather than as single-purpose solutions. He recounts inventing the next-word prediction technology used in mobile keyboards, sold to Nokia and scaled to 72 languages on Symbian Series 60 devices, and describes how ExB now applies one platform across health, mobility and finance, processing text, images and structured data in the cloud, on phones and offline in cars. He sets out three goals: reducing the cost per AI experiment, building data loops so systems keep learning from the errors a workflow corrects, and combining data modalities to gain performance and quality. Examples include extracting disease-related claims from medical publications, building a knowledge graph of automotive parts from technical documents, and a project for a German car manufacturer that predicts user behavior and answers questions about a car by mapping its internal states to language, which he calls referential semantics. He also describes closing data loops between vehicle design and after-sales repair reports, and warns that entity recognizers trained on one domain fail on another.
Key takeaways
- Assadollahi says he invented the next-word prediction technology used in most phones, sold to Nokia's Symbian Series 60.
- ExB runs one platform across health, mobility and finance, processing structured data, images and text in the cloud and on devices.
- Combining document layout analysis with text analysis helped ExB win a hackathon on extracting information from documents.
- Mapping a car's internal state to language creates referential semantics, letting a chatbot answer questions grounded in the vehicle's own condition.
- A named-entity recognizer trained on one domain, such as news, performs poorly on another, such as medical text, so systems need domain adaptation.
Detailed notes
From predictive text to a cross-industry AI platform
Assadollahi opens by explaining that he invented the next-word prediction technology found in mobile keyboards, which he sold to Nokia; it became part of the Symbian Series 60 operating system and was scaled to 72 languages on-device. Building on that background, his company ExB now works along three axes, health, mobility and finance, with a single platform that understands structured data, images and text, and that runs in the cloud as well as on phones and offline in cars.
Three goals: cost per experiment, data loops, multimodality
He frames the talk around three points he wants the audience to take home. First, companies need to reduce the cost per AI experiment so they can prioritize which use cases to pursue. Second, whenever a workflow is replaced by AI, a data loop should be built in so that the corrections users make feed back into the system and let it keep learning. Third, combining data modalities such as text and image yields performance and quality gains, which he illustrates with a system that reads medical publications and identifies that aluminium chloride in antiperspirants is linked to learning and memory dysfunction classified as an Alzheimer's-like disease.
Knowledge graphs from text, images and structured data
ExB extracts entities and relations from unstructured and structured data alike to build a knowledge graph, for example reading automotive documents to identify that braking components are connected by force. Combining a deep-learning model for document layout with text analysis let the company win a hackathon by extracting information both from document text and from its position on the page.
Referential semantics: cars that talk about themselves
For a German car manufacturer, ExB built a system that analyzes structured data from the car or smartphone to predict what a user will do next, and a separate question-answering system that explains the car's functionalities. Combining the two lets the car answer not only text-based questions but also questions about its own state, such as which warning light is on, because the car's internal state is mapped to language; Assadollahi calls this referential semantics and compares it to embodiment in robotics. He argues this is what current chatbots and voice assistants such as Siri or Cortana lack, since they have no access to the car's state.
Domain adaptation and closing the data loop
He warns that a company-name recognizer trained on one type of text performs badly on medical text and vice versa, so systems must be adapted to a specific domain, and lowering that adaptation cost matters. He describes two data loops from car manufacturing: a predictive model trained on a pre-production cohort is rolled out to individual cars and adapted per user, with usage data fed back into the system; separately, engineering descriptions used to design car parts must be mapped to the language of after-sales garage repair reports so that design and after-sales are connected. He closes by noting that machine learning is only one layer of a sustainable AI platform, which also needs data evaluation and model versioning, and that reusing document-understanding resources across use cases lowers the overall cost of scaling AI within a company.
Quotes
This kind of next word prediction was invented by me, so it's in most of the phones today.
Ramin Assadollahi
Whenever you combine data modalities like text and image, then you will get performance and quality gains.
Ramin Assadollahi
If you have a robot that has sensor data and can reason about the outer world, this is something that is approaching more towards a real artificial intelligence.
Ramin Assadollahi
Questions answered in this talk
Who invented the next-word prediction technology used in mobile phone keyboards?
According to Ramin Assadollahi at Rise of AI 2018, he invented this next-word prediction technology himself and sold it to Nokia, where it became part of the Symbian Series 60 operating system and was scaled to 72 languages on-device.
What is a data loop in an AI platform?
According to Ramin Assadollahi at Rise of AI 2018, a data loop means that when AI replaces a workflow, the corrections and learnings from that process are fed back into the system so the engine keeps learning while it is being implemented.
How can a car's chatbot talk about its own state, such as a warning light?
According to Ramin Assadollahi at Rise of AI 2018, a car's structured internal state data can be mapped into a knowledge graph together with language, so the car can answer questions like which light is on; he calls this referential semantics.