The document discusses the impact of artificial intelligence (AI) on various industries, emphasizing its role in transforming traditional business models and enhancing operational efficiency. It highlights case studies such as Kodak's fall due to technological stagnation and Red Bull's market innovation, while also addressing the impending fourth industrial revolution characterized by AI's ability to perform tasks previously reliant on human intellect. Furthermore, it outlines the necessity for companies to adopt AI technologies to remain competitive, warning that those who fail to do so risk market exclusion.

1. ARTIFICIAL INTELLIGENCE AT WORK
CRIT February 1st 2017

2. See the relatedVideo
onYouTube:
bit.ly/AI-Applications

3. UPDATE YOUR TECHNOLOGY
Some companies lose their business because they keep on doing
always the same thing and don’t realise that the world around
them has changed.
PRESENTER NOTES

4. Sometimes is a matter of moving from one technology
to another and re-inventing an old business.
Yellow Pages for example, has been replaced by online
search engines like Google
PRESENTER NOTES

5. BLOCKBUSTER failed to invest its capitals in the new
web-based business model.
Now it has been replaced by NETFLIX.
PRESENTER NOTES

6. Internet and the availability of cheap sensors and
processors determined the end of KODAK and the rise
of INSTAGRAM.
PRESENTER NOTES

7. And remember that the Italian Yellow Pages,
BLOCKBUSTER and Kodak were prosperous companies
just then years ago.
PRESENTER NOTES

8. UNDERSTAND YOUR CUSTOMERS
Some other times the technology hasn’t been changed much,
sometimes it’s a matter of adapting the product to the new
customer demand or to pay attention to the market needs and to
target new needs.
PRESENTER NOTES

9. Red Bull was the first to sell the “energy drink” concept:
here the shift wasn’t related to the product’s technology, it was more about intuition and the discovery of a new business opportunity.
Red Bull was the first to target this market and Coca Cola lagged behind.
Just after realising it they tried to recover with burn.
In a while we’ll see an example on how Deep Learning and Artificial Intelligence can be used to discover new patterns in data to target
new business opportunities.
PRESENTER NOTES

10. OPTIMISE PRODUCTION
Another application in which the Artificial Intelligence will have a huge impact is the optimisation of the manufacturing processes to achieve better quality,
lower costs.
The manufacturing systems are becoming more agile to adapt faster to the market requests.
The same production line should be made to produce smaller products batches.
The requirements are no more just about quality and speed but also on production agility.
Here the new data analysis systems should be designed to rapidly adapt to the varying production environment.
PRESENTER NOTES

11. Amazon for example has been working on big numbers from it’s birth to be competitive on prices.
PRESENTER NOTES

12. Now the advanced automation helps Amazon
in keeping low the managing costs of its
departments.
At the same time the service is much faster
and adaptable to the markets requests.
PRESENTER NOTES

13. THE 4TH INDUSTRIAL REVOLUTION
We are facing the 4th Industrial Revolution that will
strongly modify our society.
Most companies are adopting those technologies and
the first to successfully implement it will be the next
business leaders.
PRESENTER NOTES

14. The FIRST INDUSTRIAL REVOLUTION began when humans start to use FOSSIL ENERGY instead of MUSCLE POWER.
The SECOND was about mass production and line assembly. It was about the standardisation or repetitive manual tasks.
The THIRD was the first automation wave. It was in the 70’. Simple repetitive tasks were automated.
Now we see the coming of the 4th and the biggest revolution.
PRESENTER NOTES

15. This is happening now: the
companies listed in this slide
provide the fundamental
software components to create
advanced Artificial Intelligence
applications.
PRESENTER NOTES

16. And those other companies
integrate that libraries to
create vertical products on
specific markets.
Artificial Intelligence can
perform tasks that since few
years ago could be just
performed by humans.
The FIRST industrial revolution
replaced the MUSCLE POWER,
the coming FOURTH industrial
revolution will replace the
BRAIN POWER.
As soon as the major
companies will adopt these
technologies, there will be an
enormous impact on the job
market.
Repetitive tasks of
progressively higher
complexity will be automated
as the technology evolves.
PRESENTER NOTES

17. This slide shows some results
from a research made by the
University of Oxford.
In this case the telephone
salespersons and the typists
are two professions that will
be impacted by machines that
can understand the human
spoken language and relative
concepts.
This will allow to automate call
centres, customer care desks,
automatic translation.
PRESENTER NOTES

18. GoogleTranslate
This is not science fiction: you have a working example in your
pocket: just check your Google Translate or your Apple Siri.
PRESENTER NOTES

19. Other jobs to be automated
are those involving vision and
handling.
This is made possible by the
Artificial Intelligence that can
understand highly complex
scenes in cluttered
environments.
Lets see two demo video that
we made in Addfor.
In the first one we will see how
Artificial Intelligence recognise
and classify objects in a
complex situation.
In the second video, the
Artificial Intelligence describes
a picture in plain english. Just
try to image how can archives
of million of images can be
scanned and queried using
plain written questions.
PRESENTER NOTES

20. See the relatedVideo
onYouTube:
bit.ly/AI-Applications

21. IT IS NOT AN OPTION
Adopting the new Artificial Intelligence technologies wouldn’t be an option, it is the same as it was with the steam engine or the assembly line invented
by Ford: those who do not adopt this technology will simply be excluded from the market.
The development wouldn't stop: there is simply to much money involved.
NVIDIA was the best-performing stock in 2016 S&P 500 delivering a 225% total return, it increased its market capitalisation by 11 BILLION dollar just
in one day, in November 2016, the day of its Q3 earnings call.
PRESENTER NOTES

22. ALREADY HAVE SOME INTERNAL SKILL ?
ASK FOR A CUSTOMTRAINING
ASK FOR ANALYSIS AND PROOF OF CONCEPT
NO INTERNAL SKILLS ?
The adoption of a disruptive new technology can be risky and requires investments. You should take a safe path.
There are many available software solutions on the market but just some of them will fit your needs.
We are completely agnostic on hardware and software solutions, for this reason we can help our customers in making the best choices.
Maybe the best start could be to share some ideas with us.
Because the biggest risk for your company is just not being informed and not being ready.
PRESENTER NOTES

23. ARTIFICIAL INTELLIGENCE: SOME EXAMPLES
Now we will see some examples of Artificial intelligence algorithms
that are readily available to be deployed on vertical solutions.
This is just a partial list because those technologies can have a
broad range of applications, nevertheless we think that those
examples can be quite informative.
PRESENTER NOTES

24. UNDERSTAND THE MARKET
The first example is about finding Anomalies in the data streams. This class of algorithms can be applied to a wide range of problems
to find glitches in production systems, unusual behaviours in supply chains, financial systems, energy and communication networks.
Finding an Anomaly is important to trigger proper reactions, for example, it can be used for predictive maintenance or for discover
some Latent Business Opportunities in market data.
PRESENTER NOTES

25. EXAMPLE: FIND ANOMALIES INTAXI CALLS
In this case we analyse the NYC Taxi Calls Database: it contains
the number of taxi calls hour by hour in the year 2014.
PRESENTER NOTES

26. ALGORITHM FINDS ANOMALIES
BY ANALYSING RAW DATA
The algorithm has been supplied with only the raw data. No other data (like holidays or special events has been provided).
Here we see the algorithms that start to analyse the data and after a while understands that the data presents a normal seasonality and normal
behaviours at peak hours.
When the behaviour is different from what the algorithms predict the point is defined as an anomaly and is plotted in yellow or red depending of the
anomaly strength.
PRESENTER NOTES

27. FIRST ANOMALY: XMAS + NEWYEAR
The first strongest anomalies detected have an easy
explanation: on new year’s day there is a peak of taxi
calls just after midnight and on December 25th there
are few calls.
Remember that the algorithms do not have any
information about holidays, it sees Christmas just as a
standard day, for this reasons it detects an abnormal
pattern in the data.
PRESENTER NOTES

28. SECOND ANOMALY:
SUNDAY AFTER
HALLOWEEN
The second anomaly is detected for halloween and the night after
when the people get back home.
PRESENTER NOTES

29. THIRD ANOMALY:
The third anomaly was about a
strange low volume of calls the
night after the thanksgiving day. It
should be due to a bombing
thread spread from the news
PRESENTER NOTES

30. LAST ANOMALY:WHAT’S APP ON DEC 6 2014 ?
What’s strange is this anomaly detected on December the
6th: it’s difficult to spot by eyes but the system detected
here a strong glitch in data.
Seemingly nothing special happened in NYC that day.
PRESENTER NOTES

31. Just after looking back in the NYC news we found this event that
seemingly brought to NYC many unexpected visitors.
Summarising, Artificial Intelligence can watch data streams 24/7
finding irregular behaviours, faults, and useful information to
improve your systems and your businesses.
PRESENTER NOTES

32. OPTIMIZE PRODUCTION
The second example is about production optimisation. In particular we will see how
Artificial Intelligence can use the sensor’s data to predict system failures.
PRESENTER NOTES

33. EXAMPLE: ANALYSE MACHINERY DATA
The most common way to supervise machines is to create Dashboards for human operators. This approach has many limits: Time and
human attention for example. Moreover humans cannot supervise hundreds of data streams at a time.
PRESENTER NOTES

34. ARTIFICIAL INTELLIGENCE
CAN WATCH 100’S OFVARIABLES
AND FIND USEFUL INFORMATIONS
FAILUREORIGINAL
I this example our algorithm controls 100’s of variables together. Every single variable by itself is meaningless, nevertheless, we made
a system based on a Deep Neural Network that combine all the variables to find the information we need. In this case the left graph
shows the raw variables while the right graph shows the output our our system that predict a jet engine failure.
PRESENTER NOTES

35. ORIGINAL DATA
FAILURE MODES
Here the Deep Learning algorithm finds three specific failure modes indicated by the three red circles on the right graph
PRESENTER NOTES

36. OFFER NEW FEATURES
The last example is about using
Artificial Intelligence to create new
feature and set new market
standards.
In this image you can see a NEST
thermostat that set new standards
for home automation devices.
PRESENTER NOTES

37. EXAMPLE: ADVANCEDTRACTION CONTROLS
But today I would like to bring you an Italian example that explains
how Ferrari was the first carmaker to put an Artificial Intelligence
system in its production Electronic Control Units. Nowadays this
system is installed on every Ferrari model.
To explain the system in detail I could like to give the floor to Marco
Fainello
PRESENTER NOTES

38. See the relatedVideo
onYouTube:
bit.ly/AI-Applications

39. We don’t know exactly how Intelligent Machines will evolve
It will be an Exponential Growth
…
This mean that it will happen sooner than we expect

40. it.linkedin.com/in/ebusto
ThankYou