Startup pitch presented by co-founders Laure Andrieux and Nic Greenway. Aiseedo applies real-time machine learning, where the model of the world is constantly updated, to build adaptive systems which can be applied to robotics, the Internet of Things and healthcare.

1. AISEEDO
Real-­‐&me
Machine
Intelligence
HUGUK
goes
startup
-­‐
Sept
8th,
2015
Nic
Greenway,
Laure
Andrieux,
@Aiseedo
Aiseedo
confidenHal
-­‐
Do
not
distribute

2. Understanding
Hme
is
criHcal
to
understand
the
world.
Aiseedo
brings
an
understanding
of
Hme
to
Deep
Learning

3. Today’s
systems
need
to
leverage
Hme
to
funcHon
I am a
Self Driving Car

4. Systems
deal
with
various
inputs,
whose
interacHons
can
not
be
hardcoded
The
world
is
mulH-­‐dimensional
&
fast-­‐paced.

5. Why
Is
Dealing
With
Time
So
Hard?
In
the
real
world:
• InformaHon
arrives
asynchronously
• Deciding
which
informaHon
is
important
(retain
/
forget)
• Dealing
with
uncertainty
and
unknowns
• Assessing
success
is
difficult
Real-­‐&me
Machine
Intelligence
enables
smart
adap&ve
systems

6. Aiseedo delivers a streaming
Machine Intelligence service
designed to deliver adaptive,
context-aware insights

7. Aiseedo
Elegantly
Deals
With
Time
• CuZng-­‐edge
Deep
Learning
and
Recurrent
Networks
• Reinforcement
learning
for
goal-­‐directed
behaviour
• Data
fusion
from
many
asynchronous
sources
• Adapts
to
changes
in
data
without
starHng
from
scratch
• Constantly
predicts
future
and
anHcipates
events
before
they
happen
• Recognises
situaHons
and
responds
appropriately
• Copes
with
uncertainty
Integra&on
as
a
cloud
service
or
local
library.

8. Robo&cs,
Drones
IOT,
Healthcare

9. Aiseedo Real-time Machine Intelligence.
Delivered.

10. Thank
you.

11. What’s
Under
The
Hood?
Architecture
• Next
generaHon
neural
networks
delivering
highly
adapHve
Machine
Intelligence
• SAAS
The
Tech
• Streaming
Machine
learning
engines
• Data
Fusion
of
mulHple
complex
data
sources
• Real-­‐Hme,
on-­‐going
machine
learning
models
updates
Systems
IntegraHon
• API
/
web
interface
• Streaming
output
feeding
to
end
system
• Choice
of
insights:
• Suggested
acHon
/
next
best
acHon,
forecast,
predicHon,
anomaly
detecHon

12. Streaming
and
Time-­‐Series
Data
• Every
new
data
point
that
arrives
affects
our
understanding
of
the
world
immediately
• Data
arrives
in
all
kinds
of
shapes
• Not
suited
to
relaHonal
databases,
or
tools
for
analysing
tabulated
data
• Data
shapes
not
known
in
advance,
and
may
alter
unexpectedly
• Systems
must
be
fault-­‐tolerant
and
accept
noisy
data
Streaming
Data
has
par/cular
challenges

13. Temporal
Context
Is
Tricky
To
Manage
• Although
criHcal,
the
Hme
nature
of
data
is
rarely
used
Data
management:
• Preserve
Hme-­‐nature
of
entries
• Re-­‐consHtute
state
• Handle
mulHple
heterogeneous
feeds
Meaningful,
integrated
Insights:
•
Run
Hme-­‐series
queries
• …Across
mulHple
data
streams
•
AutomaHcally
plug
results
into
applicaHons
Streaming
and
Time-­‐Series
Data

14. Aiseedo Overview: Uploading Data
• JSON messages
• HTTP or websocket
• Data loaded via
‘feeds’ into
‘instances’

15. Analyse your Data via Topics
After uploading you can set up tasks for the system, allowing you to ask
questions of the data. These are set up as ‘topics’ of the following
types:
• Selector: query into and monitor a selection of incoming data
• Statistics: report statistics on incoming data streams
• Forecast: discounted future sum of a numeric value
• Prediction: what messages are expected in the future?
• SuggestedAction: what should be done next to optimise an
outcome?
• Anomaly: does something look out of place?
Multiple topics of different types can be added to your model