Matt Shearer

Data Language is proud to be featured in the "AI for Business" supplement in The
Times (10th December 2020). 

This supplement covers a range of AI solutions across a spectrum of enterprise
use cases.

To implement AI successfully
 1. You need a consistent source of reliable data to feed the AI systems.
 2. You need leadership within the organisation who understand the need to make
    faster, data-driven decisions.

The Times : "AI for Business" - 10th December 2020.Read more
 1. Raconteur AI Section
    [https://www.raconteur.net/topic/technology/artificial-intelligence/]
 2. Data Language Explainable AI Platform
    [https://datalanguage.com/products/datalanguageai/explainable-ai-platform]
 3. Data Language Text AI Services
    [https://datalanguage.com/products/datalanguageai/text-ai-services]

The Times "AI for Business" Supplement

Data Language AI for News offers a new market-leading automated news classification SaaS. It beats other SaaS because it uses your own tags.

Data Language AI for News - Automated News Content Classification

Automatically Tag your text with News Tags
We’ve trained our Data Language Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] using a News
training corpus, and are making it available for you to try out.

Simply add your text, and get News tags back. Get the latest News tags,
consistently, and improve your user journeys and SEO.

Industry-leading Auto-tagging AI
Our sophisticated Text AI automated classification service
[https://datalanguage.com/products/datalanguageai/text-ai-services] is designed
with years of experience in Data Science. It beats popular benchmarks, and
tackles all the normal data science issues faced with News auto-tagging.

Get access
Simply use the contact form to request access.

News, features and updates about our Data Language AI Products. Find out how our AI Products solve common specialist and scaling problems.

News, features and updates about our Data Language AI Products. Find out how our AI Products solve common digital transformation challenges.

Data Language AI - News and AI Product Updates

Data Language AI

Data Language AI for News - Automated News Classification

We work with organisations to deliver their ambitious production AI projects,
and we understand the need for speed, and for managing total cost of ownership.

Avoid getting stuck in "Proof of Concept" phase
In a recent survey of 1,093 industry professionals by Forbes, 64% of respondents
agree that the challenges of implementing AI have been underestimated. 61% of
the same group agree that many AI projects were having trouble getting beyond
Proof of Concept and prototyping stages.

Deliver production value, quickly and early
At Data Language, we have seen all the common issues in the design,
implementation and scaling of AI systems, and we can help you through this
process. We specialise in delivering valuable MVPs quickly and early.

Let's talk
Get in touch with us for a free assessment - we are here to help!

Delivering a Scalable AI MVP

Paul Wilton

Our Explainable AI Platform (XAI) is an adaptable product that detects violent or extremist content. It was tested and proven with the UK Home Office.

Using AI to Detect Extremist Content, at Scale, with UK Home Office



Our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] 
provides high throughput, scalable, explainable predictive analytics. It is the
ideal tool for homeland security services and content distributors to detect and
classify extremist, terrorist, or violent content.

The specialised AI deployment approach was developed for the UK Home Office to
detect Daesh propaganda, and to classify Daesh images against a set of
dimensions with high precision. 

Key Features
Scalable
Our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] is
built for scalability. Every model and algorithm deployed into the platform is
containerised and individually scalable. Queuing and constraint theory allows us
to dynamically scale models at every step in a prediction pipeline based on the
performance and throughput required of that model.

Explainable
AI should not be a black box. To engender trust it is imperative that consumers
of machine learning predictive analytics understand how the AI reached its
decision. Our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] is
designed for transparency and ethical decision making.

Evolvable
Evolvability has been baked into the architecture as a 1st class concern
providing a high degree of potential for customisation and extension. This
allows the predictive model architecture to evolve as the problem space changes
over time.

Deployable
Our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] is
deployable in cloud or on-prem, and can be used either on a fully managed SaaS
basis or as an enterprise self-managed deployment in your own infrastructure.

Analytics Dashboard
Our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] has a
rich analytics dashboard for users and moderators to analyse throughput, and
visualise the classification fingerprints of the media it has processed.

You can find out more about our Explainable AI Platform here
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform].

AI is a core discipline at Data Language. We have an expert team skilled in machine learning and deep learning, and importantly we also have the engineering and deployment skills to deliver AI at scale for your production systems.

AI is a core discipline at Data Language: Our expert team is skilled in machine learning and the engineering skills needed to deliver AI at scale.

Artificial Intelligence delivered at scale - our core discipline.

Artificial Intelligence

AI for Detecting Extremist Content

"SCOPA" is the original name for our Explainable AI Platform that provides high throughput, scalable, explainable predictive analytics. 

Explainable AI Platform - SCOPA - for high volume specialized XAI

"SCOPA" is the original name for our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] that
provides high throughput, scalable, explainable predictive analytics.

SCOPA is now called Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] 
Our platform allows us to deploy a suite of layered deep learning, ML, and
algorithmic models, in an ensemble. The results of this can be reasoned-upon to
answer very subjective questions about the input data holistically.  Models and
algorithms are individually scalable, and each may identify or predict a
specific outcome, but collectively they can answer more holistic questions. The
orchestration of the models, data analysis and pipelining is contained and
managed within the Explainable AI Platform.

Explainable AI Platform Features
Why "Explainable AI"?
AI should not be a black box. To engender trust it is imperative that consumers
of machine learning predictive analytics understand how the AI reached its
decision.

Whether for decision support or as a decision maker, an AI application must be
able to explain itself and be accountable.

Explainable AI (XAI
[https://en.wikipedia.org/wiki/Explainable_artificial_intelligence]) is a
burgeoning field in machine learning that tackles this challenge, so we, as
ultimate end-users, have faith in machine intelligence and can strive for its
continuous improvement. Our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] 
embraces explainability, providing insights into the decision making process of
the predictive output that it has made in answering questions of the data,
ultimately providing a fingerprint of the predictive qualities.

Scalable
Our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] is
built for scalability. Every model or algorithm deployed into the platform is
containerised and individually scalable. Queuing and constraint theory allows us
to dynamically scale models at every step in a prediction pipeline based on the
performance and throughput required of that model.

Evolvable
Evolvability has been baked into the architecture as a 1st class concern
providing a high degree of potential for customisation and extension. This makes
it an extremely portable solution across diverse domains, and also allows the
predictive model architecture to evolve as the problem space changes over time.

Deployable
Our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] is
deployable in cloud or on-premises, and can be used either on a fully managed
SaaS basis or as an enterprise self-managed deployment in your own
infrastructure. It also uses contemporary open-source architecture technologies,
with full deployment automation. It can even be deployed on laptops for
demonstrations or at-the-edge applications.

Use Cases
Our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] is
ideally suited to enterprise use-cases in a number of sectors, such as
specialised media classification, industrial maintenance, agri-tech, and
healthcare.

It has been deployed to classify terrorist and extremist propaganda imagery for
the security services and the Home Office in the UK at scale. 
Even for such a complex and multi-faceted challenge, a bare bones instance of it
scored remarkable metrics (over 0.95 precision, 0.85 recall) with plenty of room
for substantial improvement.

Our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] can
be deployed for use cases in industry such as railway track environment analysis
, or for predictive maintenance.

In Agri-Tech is is suited to use-cases such as crop disease prediction, yield
prediction, or pest analysis using for example, drone footage, or spraying-boom
camera imagery.

In healthcare it can be used for novel image analysis such as bone fracture
detection. 

Please do get in touch if you would like to find out more.

SCOPA - scalable, explainable AI

Our Text AI Services for Publishers provides auto classification, recommendations and entity extraction. The engineering and data science is taken care of.

Text AI Services for Publishers - highly optimized scalable AI

Our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] were
originally designed to meet the challenges of automated content workflows in the
publishing industry, and are ideally suited to classification and tagging of
news, sport and entertainment content.

Why do publishers tag their content?
Most large publishers need to tag or classify their content for a number of
reasons :

 * to assist internal processes and work-flows
 * to improve search and discovery
 * to provide richer content and user analytics
 * to drive advertising
 * to deliver richer user experiences

Typically tagging is a manual task, integrated into the editorial workflow and
the CMS. Increasingly, publishers are working to automate this process using NLP
and machine learning techniques.

Automated content tagging
Automating content tagging unlocks a lot more value, as the automated-tagging
system can be used to

 * improve tagging efficiency, consistency and quality on the news desks
 * fully-automate tagging of external news feeds
 * unlock value in long-tail content
 * automate tagging of other content around the organisation

For this reasons, publishers are investing large sums and time in building their
own automated tagging systems. However what may seem like a simple problem in
the R&D labs can turn into a much more complex and expensive problem, when put
into production.

How Data Language Text AI Services can help
O [https://datalanguage.com/tagmatic]ur Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] are an AI
and Machine Learning fully managed SaaS product. We built them to make it as
simple as possible to integrate automated tagging and classification into your
publishing systems, addressing all the key challenges :

 1. It is incredibly easy to integrate. Send it content to learn. Send it
    content to tag.
 2. It uses your own dictionaries, taxonomies, and classification vocabularies
    out of the box.
 3. The engineering challenge of scaling, retraining, and efficiently
    redeploying the machine learning models is done for you.
 4. It learns on the fly. As new terms arise in news, our Text AI Services
    [https://datalanguage.com/products/datalanguageai/text-ai-services] learn
    them quickly.

Total cost of ownership
Building you own AI based tagging solution is no mean feat. It is not just a
data science problem, but involves software engineering, testers, DevOps,
infrastructure, scaling automation, model deployment automation, continuous
machine learning model training automation, as well as the project management
and business processes that accompany a project of this nature. Both the total
cost of ownership and time-to-deliver is substantial.

With our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] you can be
up and running in hours, not months, at a fraction of the total cost and you can
put your data scientists to work on your market differentiating data outcomes
instead.

Get in touch
Use the contact form to get in touch - we are here to help.

News and product updates about our Text AI Services. Read about how our Text AI products are being used in innovation projects, and by our clients.

Read the latest on our Text AI Services. Projects, Innovation initiatives, and new developments for AI features in specialized use cases.

News and Product Updates about our Text AI Services

Text AI Services

Text AI Services for Publishers

Tolga Ergin

A great AI or ML model is worthless if it can't be delivered in a scalable, cost effective and technically sustainable way. Find out how.

Data Science at Scale - 10% AI, 90% Engineering

A great AI or ML model is worthless if it can't be delivered in a scalable, cost
effective and technically sustainable way. Our data science team excels in the
design and implementation of machine and deep learning models for the analysis
of data and media at scale.

Data Science seems to have something mystical about it, to the extent that
enterprises have almost come to consider it as a magical solution to impossible
problems. The reality is that behind DS there are tons of unfashionable data
engineering and analysis tasks, before statistical models can be applied to the
problem. 

Our team of talented experts here at Data Language can leverage years' worth of
technical excellence to ensure your AI solution can actually be scaled and
delivered at the highest standards of software engineering.

Understand your data 
Unlock the full potential of your data through early discovery, analysis, and
visualisation.

Learn fast
With so many variables to tweak, rapid feedback and short iteration cycles are
imperative to an effective delivery.

Models are software
As with any piece of code, statistical models also benefit from engineering best
practices for delivering at scale, with controlled total cost of ownership.

--

Read our white paper on rethinking how you do data science in your organisation:

 
[https://datalanguage.com/static/whitepapers/rethinking-data-science-in-the-enterprise.pdf]

Data Science at Scale

Data Language AI Logo on Dark

Our Text AI Services solve the common AI SaaS pain points: they are specialized to you, language agnostic, self-optimizing, and immediately scalable.

Text AI Services - Data Language AI

Our Text AI services use machine learning to make tagging, entity recognition,
auto classification and content recommendations both easy and efficient. 

They dramatically reduce the time, cost and complexity of organising your text
content. The data science, difficult engineering and scaling are all taken care
of.

Our Text AI classifiers and entity extractors are enterprise grade, state of the
art machine learning, delivering best-in-class capability.

SaaS Services
These SaaS services come with powerful APIs for immediate integration:

 1. AI Text Classification - classify your content using your taxonomies
 2. AI Entity Extraction - document level and inline
 3. AI Entity Relation Extraction - find relationships between entities inline
 4. AI Recommendations - fully automated content recommendations

Advantages
The real advantage with our Text AI services, is that they dynamically spin up
new models as they are needed, without the need for any data scientist. 

As fully managed SaaS products, our Text AI services are:

 1. Easy to integrate
 2. Language agnostic
 3. Use your own vocabularies
 4. Self-optimise on the fly, independent from your workflow
 5. Highly scalable, highly performant
 6. Automated on-the-fly training 
 7. Minimum setup time

This platform enables explainable AI and scales out of the box to meet your needs and evolve rapidly. Use your own terms, and move quickly to scale.

Explainable AI Platform - Data Language AI

Our specialist Explainable AI Platform orchestrates suites of predictive
analytics models to answer complex questions in computer vision, media
classification and data analysis.

It uses ‘layers’ of ensemble machine learning models to make these deductions.
For example to predict the likelihood of:

 * a social video being extremist in nature, or
 * a certain movie script or video meeting a relevant film classification.

Our platform is a response to the expensive-to-produce, expensive-to-maintain,
difficult to maintain selection of current products on the market, which
resemble ‘black boxes’ with limited transparency as to the predictions made…

By contrast, our Explainable AI platform is extremely scalable – and the
insights into how predictions are made – are clear!

We are here to help
Simply use the contact form to reach us - we would love to hear about your
project!

Explainable AI Platform

Data Language AI is our suite of intuitive AI products that generic SaaS AI services cannot beat. It is specialized, scalable and evolvable.

Data Language AI - Artificial Intelligence Engineered

Our AI Services
 1. Text AI Services
    [https://datalanguage.com/products/datalanguageai/text-ai-services]
 2. Explainable AI Platform
    [https://datalanguage.com/products/datalanguageai/explainable-ai-platform]

Market Leading AI for Digital Transformation
Artificial Intelligence prediction and automation tools are now important
components of your digital transformation journey.

The team at Data Language have developed market-leading AI services to support
your business.

Data Language AI Strengths
 1. Specialized
 2. Scalable
 3. Evolvable

Data Language AI Suite

Data Language AI Strapline

Data Language AI Logo

We are developing our Text AI Media Classification Products to help Cochrane automatically classify Clinical Studies onto Snomed-CT and PICO structured data

AI Classification of Clinical Studies with Text AI to fight Covid-19

We've just received the green light on this public-funded innovation project,
bringing two areas of significant Data Language expertise into play to help
combat Covid-19.

The mission combines two of our favourite things:
1. Clinical Evidence Knowledge Graphs
We've been working with Cochrane on their knowledge graph and data strategy
[https://datalanguage.com/blog/how-knowledge-graph-technology-is-helping-cochrane-respond-to-covid-19] 
since we started the project with them in 2014. As part of this, we recently
helped them launch their Covid-19 Study Register
[https://datalanguage.com/blog/covid-19-rapid-response-technical-delivery-at-cochrane]
, which summarises the current state of Covid-19 clinical evidence from around
the world that has been released into the public domain via authoritative trial
registries and journal databases.

2. Our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] for
automated text classification
Over that time, we have also cemented our position as industry leaders in
machine learning text classification and analytics, and have developed
particular expertise in high volatility domains (e.g. academic research,
breaking news, R&D) and complex ML engineering challenges. This led us to
creating our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services].
[https://datalanguage.com/tagmatic-ai-document-classification]

Mission: to ensure Covid-19 clinical decision making is informed by the latest,
most comprehensive reliable evidence possible.
With the help of the UKRI [https://www.ukri.org/], Digital Catapult's Machine
Intelligence Garage
[https://www.digicatapult.org.uk/for-startups/acceleration-programmes/machine-intelligence-garage]
, and Innovate UK [https://www.gov.uk/government/organisations/innovate-uk], we
are now bringing our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] into play
with our subject matter expert partner Cochrane to help the fight against
Covid-19.

We will be launching this new HealthTech AI beta before December. 

Get in touch if you want to know more.

Automated Classification of Healthcare Evidence

BOTT is a data ethics framework for the practical assessment of AI and predictive analytics models within their ecosystems; Bias, Outcome, Target, Trust

BOTT Framework for Practical AI Ethics - Bias, Outcome, Target, Trust

As a data company, data ethics has always been important to us, but in the last
few years, as Machine learning and AI have become front and centre as a
significant part of digital transformation in the enterprise, data ethics has
become a hot topic. It bears such a considerable weight in predictive analytics,
not just in conveying trust to consumers of machine intelligence, but also in
ensuring predictive models deliver genuine value, make unbiased predictions, and
reflect the true state of the environment within which they live. When data
ethics go wrong, it can go badly wrong, potentially bringing down an entire
organisation, with substantial direct and indirect collateral damage,
potentially even on national democracy and social cohesion - the Cambridge
Analytica incident a case in point.

We all need to take it seriously.

In 2018 Data Language was privileged to have been accepted onto the Digital
Catapult “Machine Intelligence Garage
[https://www.digicatapult.org.uk/for-startups/acceleration-programmes/machine-intelligence-garage]
” programme. As well as supporting us with a terrific programme including
compute resource, strategic help and industry engagement, Digital Catapult also
took Data Language through their ethics programme. This was like a cold-shower,
really waking us up to depth and breadth of data ethics. A realisation that data
ethics is not just about data (technically), but about the way you approach your
entire business. 

With all this in mind, we were motivated to develop a data ethics framework that
we could use internally at Data Language to assess all our predictive analytics
projects, and that could guide us practically during implementation. We call
this framework BOTT.

The BOTT Framework
BOTT (all good frameworks need a memorable acronym) stands for Bias, Outcome,
Target and Trust. Through these four pillars, it allows our data science team to
examine, and assess a predictive analytics model within its local and external
ecosystem for quality, bias, and accuracy, and ensure it meets high standards of
data ethics.

Bias
An analysis of potential bias that may be baked into the model during its
life-cycle, either literally through data or code, or intangibly through for
example, some conscious or subconscious human bias. We examine bias from the
following perspectives :

Training data bias - is the training data diverse enough and comprehensive
enough to accurately reflect the environment of the predictive model. What
potential biases might skew predictive results either for good or bad outcomes -
is there any chance these biases might exist in the training data we have used.

Algorithmic bias - is the model algorithm, its mathematical basis, or code
potentially biased?

Real-time input data - if real-time (predict time) input data is involved in
making some prediction, what might these data biases be, and how can we prevent
them from impacting outputs.

Team bias - has or could conscious or unconscious bias of the model development
team contribute to model bias, and have we mitigated against it. Often just an
open and frank team discussion around bias (with respect to the project under
consideration) is enough to flush out these sort of issues

Political, environmental, and social bias - similar to the team bias but looking
at potential external factors through a similar lens - for both ideally the team
needs to be very comfortable talking about potentially politically or socially
sensitive topics in an open, friendly and non-defensive manner.

Output sampling - essentially QA - have you done effective output sampling and
for quality assurance - does the risk of bias in the predictive model call for
independent third party reviews in QA workflows, etc

Outcome
An analysis and survey of the full range of possible outcomes of the model:

Catalogue - construct a catalogue of the potential outcome scenarios

Impact - What does success and failure of the model mean :

 * in the context of the target use-cases
 * best and worst case scenario modelling
 * model failure consequences, e.g. could somebody die if the model fails ?

Target
Analysis of who or what the model is targeted at and the context in which these
users operate.

Users -  Who are target users of the model  - who or what is the direct consumer
of the model ?

Purpose - What problem is the model solving?

Context: Is it part of an ensemble of models, is there a wider predictive
ecosystem the model sits within - what impact does this model have within this
context ?

Life span -  What is the life expectancy of the model, and how does its
environment change over time

Subversion - Can other users or consumers subvert the model for
unintended/undesirable ends?

Catalogue - Construct  a catalogue of potential unintended uses/consequences,
and ascertain if those vulnerabilities can be designed out of the architecture?

Trust
Providing some visibility of the "behind the scenes" implementation can raise
confidence in the models and foster a trust relationship between consumers and
predictive services.

Explainable AI (XAI) is becoming a field in its own right, and an important part
of machine learning and predictive analytics. Codable frameworks are emerging to
aid explainability (for example Google Model Cards
[https://modelcards.withgoogle.com/about]). This codability will be key in being
able to compare and assess models through some normative evaluation mechanism.

Transparency - what should we be exposing of the lineage and decision making
pipeline of the model prediction algorithm :

 * training cycles / training samples
 * outputs of intermediate predictions
 * decision inputs/outputs

Impact - Why should these be exposed ? - perform an impact analysis of the
possible effects of not exposing model lineage, with respect to legal,
environmental, and model improvement consequences.

Data Ethics

The BOTT Framework for Practical Data Ethics

Analysis of 1 month of video stats from Jamie Oliver Christmas videos shows 10x CTR on Data Moments, and extremely high engagement rates.

Data Moments shows benchmark-beating stats on Jamie Oliver videos

Data Moments is a new type of video experience, where users can interact with
moments in video. For Jamie Oliver, this means getting more from each step in a
recipe video, and highlighting products that are used in the video so that
viewers can buy them directly.

This image shows a "Making thyme flavoured oil" video moment, that had an
interaction rate of 62%.From a data sample of 1 month of Data Moments on Jamie Oliver video
[https://datalanguage.com/news/jamie-oliver-christmas-videos-enhanced-with-luminery]
, here are some highlights:

Video Moment Engagement
 * Challenge: Video experiences haven't changed for years. Can we get a new,
   user-centred experience that dramatically improves how viewers use Jamie
   Oliver videos?
 * Solution: Identify steps in the recipe, and the techniques, products and
   ingredients in those steps. Activate the steps as structured "How To" moments
   using Luminery. Add important products so that users can find them directly.
 * Results: 92 new "moment interactions" per 100 video views. 9.3% Interaction
   Rate with HowTo steps (compared to the current ZERO).

Video Marketing Conversion Performance
 * Challenge: Jamie Oliver produces a range of products, and creates incredible
   recipe videos. How do we drive better product sales from these videos?
 * Solution: Identify products in the recipe and activate the moments to help
   users directly reach and buy the products at the right moments using Data
   Moments (saving huge conversion fall-off due to searching and uncertainty
   barriers).
 * Results: 5.4% CTR (clicks as % of video views) generated by the "product
   moments" (compared to 0.5% of standard pre-roll Ads). 9.6% conversion rate at
   the Ecommerce vendor destination for Jamie Oliver products.

What does this tell us?
This demonstrates, again, the benefit of using structured video moments to
activate new value in Video. We've demonstrated new engagement and revenue
-driving opportunities, and a fresh model for video publishing.

News, features and updates about our video moment product Data Moments. Find out how this innovative product harnesses structured data in video. Brought to you from the team that was foundational in the earliest large scale linked data projects in the UK.

Read news, features and updates about our video moment product Data Moments. Find out how this innovative product harnesses structured data in video.

Data Moments - News and Video Product Updates

Data Moments

Data Moments - Get the latest on this Video Data Moment Product

Data Moment video Stats from Jamie Oliver Christmas videos

Who is Cochrane?
Cochrane [https://www.cochrane.org/] is the global not-for-profit organisation
whose core mission is to put evidence at the heart of health decision-making all
over the world. Over 90% of WHO guidelines are informed by Cochrane’s systematic
reviews.

What is the Covid-19 Study Register?
The Register is a precisely scoped, authoritative source of the latest clinical
evidence from around the world concerning the Covid-19 pandemic, that is being
used to inform point-of-care clinical decision making and evolving healthcare
policy internationally.

Healthcare professionals can use it to locate all relevant, reliable clinical
evidence about specific healthcare questions from the latest Randomised
Controlled Trial (RCT), observational, modelling and other studies to support
the fight against Covid-19.

This crucial, trusted resource is essential at a time when the evidence base is
developing rapidly, as healthcare professionals require structured and reliable
up-to-date information.

Partners with Cochrane since 2014
We first worked with Cochrane in 2014 on a digital transformation project,
designing and delivering a radical new platform architecture centred around
linked-data and knowledge graph technologies. Since then we have worked to
refine, iterate and scale their platform to create a suite of applications to
publish evidence in healthcare using structured data.

Solid data architecture makes rapid innovation possible
This Covid-19 Studies Register is the latest example of how Cochrane is able to
innovate rapidly on the scalable knowledge graph platform architecture delivered
by Data Language. The structure and flexibility means that new use cases can be
delivered rapidly and efficiently.

See the Covid-19 Study Register here [https://covid-19.cochrane.org/].

Find out more
 * Cochrane's Covid-19 Resources and News
   [https://www.cochrane.org/coronavirus-covid-19-cochrane-resources-and-news] 
   (page updated regularly).
 * More about Cochrane and the rapid delivery of the Covid-19 Study Register
   project
   [https://datalanguage.com/blog/covid-19-rapid-response-technical-delivery-at-cochrane]
   .
 * Our work with Cochrane since 2014 - Information Architecture, PICO Ontology
   and more
   [https://datalanguage.com/blog/how-knowledge-graph-technology-is-helping-cochrane-respond-to-covid-19]
   .
 * More about our Knowledge Graph Engineering
   [https://datalanguage.com/knowledge-graph-engineering].

A knowledge graph is a collation of information and data typically within the domain of your business context. The information is stored in a database, often a graph database such that it can be queried, maintained, and interacted with by humans or machines. We have exceptional experience in the design, implementation and deployment of knowledge graphs. We have been working with graph databases since our inception and have delivered knowledge graphs solutions for global organisations.

We have exceptional experience in the design and implementation of knowledge graphs and have delivered knowledge graphs solutions for global organisations.

Knowledge Graphs - our Work, Features, News and Updates

Knowledge Graphs

Launching the Cochrane Covid-19 Study Register

Our pitch included using our Explainable AI Platform to provide London's analysts with explainable AI (XAI) decision support.

Explainable AI Platform at London Civic Innovation Challenge

We've just been at City Hall for London's Civic Innovation Challenge Awards
Ceremony. Sadly we didn't win on this occasion, but we were proud to be in the
finalists for the Counter Violent Extremism Social Tech Challenge.

Explainable AI Social Tech to help Londoners
Our pitch included using our Explainable AI Platform
[https://datalanguage.com/products/datalanguageai/explainable-ai-platform] to
provide London's analysts with explainable AI (XAI) decision support.

Explainable AI decision support - in this case for violent extremism specialists
Thanks to the GLA Team and Social Tech Trust!
The finalists with the GLA team and Social Tech Trust.

News and Product Updates about our Explainable AI Platform (XAI)

Explainable AI (XAI) means that the process, reasoning and decisions made by the AI are transparent and clear to the users and teams involved. Even before this became a trending technical jargon term, we were delivering Explainable AI products and services.

Updates and news about our Explainable AI Platform (XAI). We are specialised in moving new AI services to production rapidly, avoiding pain points in XAI

News and Product Updates about our Explainable AI Platform (XAI)

Proud Finalists in London's 2020 Civic Innovation Challenge

Data Language chosen for the Scaleup Programme.
We are delighted to announce that we have been chosen for the Innovate UK and
EEN Scaleup Programme. This programme supports a select group of companies on a
journey to exponential growth, providing them with countless opportunities with
bespoke, fully-funded one-to-one support.

We are immensely proud of this achievement as admission onto the Scaleup
Programme is a highly selective process. For the first cohort 2,800 companies
made up the initial long list of applications with only 30 selected, so to be
chosen as one of the successful companies in the new cohort has given us even
greater confidence in our ambition.

We can’t wait to get to work with the Scaleup team to start bursting through
barriers to growth and taking Data Language to its full potential.

Scaling Up

The Jamie Oliver team is using Data Moments - our structured data video moment SaaS - to create new "How To", promotion and Product promotion video moments.

Jamie Oliver uses Data Moments for Christmas Videos

We're really pleased to announce the launch of the Jamie Oliver Christmas "How
To" video feature, powered by Data Moments
[https://datalanguage.com/products/datamoments], our SaaS structured data video
moments platform.

Jamie Oliver is using our platform to activate and enhance the valuable moments
within his Christmas videos.

Data Moments contextual commerce "Product Moments"
A product promotion "moment" on Jamie Oliver's Christmas Video Feature.Data
Moments "How To" video moments.
The Jamie Oliver team are interested in whether users used videos "to cook
along" - we are using our HowTo Video Moments to help users navigate between
important steps.

Jamie Oliver uses Data Moments

In September 2019, we enhanced Rugby World Cup video using our Data Moments product to activate their video. 50% of their key moments were in Google top 10

Video Micro-Moment Performance in Rugby World Cup 2019

Data Moments [https://datalanguage.com/products/datamoments] is a new type of
video experience, where users can interact with moments in video. These are
unique opportunities we call Video Micro-Moments, where we can use Data Moments
to meet user needs
[https://datalanguage.com/video-micro-moments-meet-user-needs-at-their-strongest] 
in the actual moment that they occur.

For the Rugby World Cup in 2019, this means enabling rugby fans to discover,
navigate and enjoy the great sporting video moments with ease.

Video Moment Reach
 * Challenge: J SPORTS have unique sporting moments within their videos, that
   cannot be discovered by search engines or users with normal video publishing.
 * Solution: Markup the key sporting events, teams and stadiums with Data
   Moments [https://datalanguage.com/products/datamoments] structured data video
   key moments that are optimised for SERPs (Video SEO).
 * Results: 45% of video moments created for J SPORTS Rugby World Cup ranked in
   the first page of Google SERPs (Search Engine Results Pages). This created a
   completely new and significant SEO surface area, growing reach by 37%, and
   creating Google Discover opportunities too (with 4.2% CTR), 

Video Marketing Conversion Performance
 * Challenge: J SPORTS run an E-Commerce store selling sporting goods, plus a
    Sports SVOD product, and want to increase the traffic from video highlights
   through to conversions.
 * Solution: Create "Buy in the moment" contextual commerce moments, linking
   directly to products at key video moments, meeting the user's "I want to buy"
   video micro-moment need.
 * Results: 1.1% CTR - significantly beating the 0.5% standard for conventional
   video advertising.

Read the latest news and updates about Data Moments video key moments
[https://datalanguage.com/about/datamoments].

Data Moments Stats - Video Key Moments in the Rugby World Cup

J SPORTS is using Data Moments - our video key moment SaaS product - to activate video key sporting moments and brand moments for the Rugby World Cup.

We're really pleased to announce that J SPORTS is using Data Moments
[https://datalanguage.com/products/datamoments] (our SaaS Video Enhancement
Platform) to enhance video moments for their Rugby World Cup highlights.

We have been working on this project with our innovation partners at EAI
[https://www.entertainmentai.co.uk/] to help J SPORTS use our innovative video
moment enhancement technology for SVOD, E-commerce and Sport Event micro-moment
promotions.

Why is J SPORTS using Luminery?
J SPORTS were particularly excited about the Data Moments
[https://datalanguage.com/products/datamoments] activation of video moments that
effectively meet user needs exactly when they arise. Google's marketing
evangelists call these moments "Micro moments
[https://www.thinkwithgoogle.com/marketing-resources/micro-moments/micro-moments-understand-new-consumer-behavior/]
".

Sporting Event Micro-Moments - "I want to know"
With Data Moments [https://datalanguage.com/products/datamoments], each sporting
moment - tries, conversions, line-outs, penalties etc - has a new opportunity
listed on SERPS, shareable, and brand activated.E-Commerce Micro-Moments - "I
want to buy"
With Data Moments [https://datalanguage.com/products/datamoments], each brand
activated moment can feature Contextual Commerce activated promotions for
products, when they are actually relevant to the user!J SPORTS Enhanced Video
Homepage
At: video.jsports.co.jp [https://video.jsports.co.jp] - the videos are only
available for users in Japan, due to RWC rights restrictions.

The J SPORTS With Data Moments [https://datalanguage.com/products/datamoments] 
Video HomepageGet in touch [https://datalanguage.com/contact-us] if you want to find out more.

Data Moments activates video moments for The Rugby World Cup

Tom Fox from the "Hi Growth Unit" at KPMG awarded us with the KPMG Prize at
Venturefest in Oxford.

Selected from an impressive spread of startups 
The pitching participants.Luminery Demo
The team demonstrate Luminery enhanced video moments at Venturefest

News and Product updates about Luminery, our state-of-the-art Video Micro-Moment SaaS product. Luminery enables Publishers, Brands and Creators to leverage the value of video micro-moments for increased reach, revenue and engagement.

Read News and Product updates about Luminery - the Video Micro-moment SaaS Platform that increases video reach, engagement, revenue and social capabilities.

Luminery - The Video Micro-Moment Platform - News & Product Updates

Luminery

Luminery wins KPMG Prize at VentureFest

Data Language has been accepted onto the Digital Catapult Machine Intelligence
Garage [https://www.digicatapult.org.uk/projects/machine-intelligence-garage] 
program.

This is fantastic news for us. Through MI Garage, Digital Catapult is going to
provide us with plenty of high performance infrastructure and technical support
to power our machine learning and AI product R&D throughout 2019.

This will allow us to rapidly advance innovation and development across all our
products, running our ML models at scale, and on GPU kit. It will enable us to
expand our Tagmatic [https://datalanguage.com/tagmatic] ML product suite for
automated content classification; our deep learning image classification
platform, SCOPA; and help with automation on our Contextual Commerce platform, 
Luminery [/2019/02/06/digital-catapult-machine-intelligence-garage/luminery.com]
.

2019 is going to be a great year for Data Language!

Digital Catapult - Machine Intelligence Garage

We had some more great news. Innovate UK
[https://www.gov.uk/government/organisations/innovate-uk] has awarded Data
Language a new innovation grant for further funding experimental development and
proving the market of our Luminery [https://luminery.com] platform to deliver
crowd and machine sourced contextual commerce- and brand enhanced- video
moments. 

Our aims are to drastically improve the advertising revenue publishers can make
from video, to improve discovery and reach at a video moment -level, and also to
provide a fair reward mechanism for crowd workers.

You can see a working prototype [https://luminery.com/food-example] here
demonstrating how our contextual commerce works.

Innovate UK

Innovate UK funding for Luminery

Tagmatic is a service for automatically tagging your content.
It uses sophisticated machine learning techniques to learn how you tag your
content on-the-fly and then will tag new content automatically.
Tagmatic uses your own tag dictionaries. Whether you tag with simple text terms,
identifiers, taxonomies, or linked data, Tagmatic will tag with those. There is
no data mapping exercise needed, just send your content & tags to Tagmatic and
it gets to work. Tagmatic quickly learns new tags too.
It is incredibly easy to integrate into your CMS or editorial workflows via two
simple web service interfaces.
If you are looking for an automated tagging solution, then look no further.
If you are developing your own automated tagging solution, then pause, give
Tagmatic a try - and be up and running quickly.

If you would like to try Tagmatic on your own content, get in touch
[https://datalanguage.com/tagmatic]  for a free trial.
For more information go to https://datalanguage.com/tagmatic

News, Blog Posts and Innovation updates about Tagmatic - our sophisticated AI automated content classification SaaS product. We have taken care of all the engineering and data science for you: Tagmatic learns on the fly, is self-optimising, and language-agnostic.

Tagmatic is an AI automated text classification SaaS product by Data Language. It learns on the fly, is self-optimising, and language-agnostic.

Tagmatic - News and Product Updates about our Text Classification AI

Tagmatic

Introducing Tagmatic

We had some fantastic news this week. Innovate UK
[https://www.gov.uk/government/organisations/innovate-uk], part of the UK
Government Technology Strategy Board has awarded Data Language an innovation
grant to fund the development of a platform to deliver crowd and machine sourced
contextual commerce and structerd data on video moments.

Our aim is to structurally enhance videos at a moment level, which will then
improve the revenue publishers can make from video. The approach will also
provide a fair reward mechanism for crowd workers.

Watch this space!

Proud Finalists in London's 2020 Civic Innovation Challenge

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