We are living in the era of "the fourth industrial revolution". How did we get here? Read this presentation to explore current application trends in Artificial Intelligence (AI,) The Internet of Things (IoT), Big Data, and Machine Learning (ML) technology. Also, to discover the future implications of big data in our lives. Read the original article here: https://www.pangea.ai/data-science-resources/future-of-data-science/ Work with a data science expert at Pangea: https://www.pangea.ai/

1. A Glimpse Into the Future of
A Glimpse Into the Future of
Data Science – What's Next for
Data Science – What's Next for
AI, Big Data & Machine
AI, Big Data & Machine
Learning?
Learning?
by Polina Tibets
Head of Business Ops @ Pangea.ai Date: 25th of October, 2021

2. We are living in the era of
We are living in the era of
"the fourth industrial revolution"..
"the fourth industrial revolution"...
.
Let's explore what that means
Let's explore what that means ▶
▶

3. The Internet of Things
(IoT)
Machine Learning
Big Data
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New technological trends like:

4. ... are ushering in changes so profound that, from the perspective
of human history, there has never been a time of greater promise
or potential peril.
Klaus Schwab
Executive chairman of The World Economic Forum 🌍
“
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5. •
•
•
A brief history of the Fourth Industrial Revolution
Current application trends in AI, IoT, Big Data, Machine
Learning technology
Future implications of big data in our lives
In this presentation, we will explore...
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6. How did we get here?
How did we get here?

7. Bayes' Conditional
Probability Theorem -
18th century
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Data science theories have existed for hundreds of years:

8. Gaussian Distribution
and Markov Chains -
19th century
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Data science theories have existed for hundreds of years:

9. Pearson Correlation
Coefficient -
20th century
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Data science theories have existed for hundreds of years:

10. A brief history of computing
•
•
In the early 20th century, computing was made by
electromechanical machines, which required
physical movements to execute simple operations:
Proto-computers were able to compute less than
0.001 calculations per second.
Data storage capacity was 10 megabytes in
1980.
It was not enough required to produce artificial
intelligence.
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11. But since then, computing capacity has
grown exponentially. Today's computers
have:
•
•
•
5 billion calculations per second: the speed of
computing on Intel's Core i7 QUAD processor
Hundreds of Gigabytes of data storage –
excluding cloud storage, which is infinite
1000 times more international bandwidth than
in 2004 (when it was less than 1 Terabyte per
second)

12. Evolution of data science and machine learning terms search on Google since 2004:

13. Ray Kurzweil: "The singularity is near: When humans transcend biology"

14. Massive bandwith growth has enabled
Massive bandwith growth has enabled
our constant connection online via
our constant connection online via
phones, tablets, smart TV, watches,
phones, tablets, smart TV, watches,
and computers.
and computers.

15. Our online connections continuously
generate an enormous amount of
information.
So, where does it all go?
1.
2.
It is sent to cloud servers and saved for later
analysis.
It is harnessed to train complex mathematical
models that can predict future behaviour.

16. Data science shapes our reality
In what key areas is AI being used to shape
existing services?
Let's look at current application trends ▶
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17. Social Media
Social Media

18. Your actions are tracked on social networks like TikTok, Instagram and Facebook.
They are then used to create predictive machinery.
This in turns, informs the kind of content you are being shown.
Social networks are constantly learning your preferences, tastes, and opinions
to tailor the content to you.
The more your engagement on social apps is maximised =
the more these networks increase their returns.
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19. Sports Analytics
Sports Analytics

20. Advances in technology—like chips placed in players’ shoulder pads—allow teams to measure the
exact speed and position of a given player on the field.
How data science is making the National Football League (NFL) smarter
Access to this information generates radical changes in sports
strategies: from player and scheme fit, to training and fitness
monitoring.
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21. NFL team Philadelphia Eagles uses big
data for:
•
•
•
Tracking
Math-based decisions
Statistical analysis (the most important)
The team is incorporating these big changes into
in-game decision making.

22. 🏈In most fourth-down situations, it’s preferable to risk a final play than to kick a field goal or punt
the ball away to the opponent
🏆 The coach must listen to the analyst's advice on the field – as seen from the “Philly Special” trick
play that helped them win the Super Bowl
The Eagles became one of the most aggressive teams on fourth
down – securing their first-ever Super Bowl in 2017 🦅
What did they learn from applying statistical analysis to their games?
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23. Other NFL teams adopted this strategy,
resulting in a 40% increase in fourth-down
attempts between 2017 and 2019.
With suboptimal fourth-down strategy
becoming widespread, analytics will
become even more in demand in football.
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Ripple Effect in the NFL

24. Mental Health
Mental Health

25. In psychiatry, diagnosis and treatments are some of the most complex tasks. The absence of
precise and objective clinical tests, similarly used in other areas of medicine, results in many
patients being misdiagnosed.
World Psychiatry – the prestigious scientific journal – published a study and suggested a hopeful
solution.
How Machine Learning (ML) is helping to diagnose and treat mental illness
How do they propose to solve this problem?
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26. 🔺More than 2 years in advance
Using a Machine Learning model that can detect a psychotic breakdown with:
🔺An accuracy rate of 83% – which is 53% higher than the rate obtained by doctors
🧠The use of a natural language processing (NLP) model that quantifies the typical
deformation of language for patients with schizophrenia
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27. The same NLP technique can be applied to
diagnose and treat other conditions like
Parkinson's, dementia, depression, or
bipolar disorder.
AI has the potential to make very real
benefits for healthcare.
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Waves of change in healthcare

28. Did you know that NLP is one of the most popular
areas of Machine Learning?
It is used by virtual assistants like Siri and Alexa –
automating life for millions of people on the
planet, daily.
NLP application outside of healthcare

29. Criminal Law
Criminal Law

30. ML was introduced in 14 states to boost the productivity of judges by automating "routine" jobs,
like setting sentences.
Tools like COMPAS analyse historical data from US prisons to predict the probability of a subject
repeating a crime. To do so, they consider criminogenic factors (causes of crime) of populations
with similar characteristics to the value.
How AI inadvertently uncovered systemic bias in the United States' criminal law
A group of analysts tested the ML model. What did they discover?
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31. The ML system has a clear bias.
The algorithm predicted a much higher
recidivism score for black people than
white people.
The reason behind this bias is the sad fact that
prison populations are disproportionately black in
the United States, due to its complex history of
racism.
So in this case, the automation unintentionally
uncovered systemic bias in the historical rulings of
judges that the algorithm was trained on.

32. In Conclusion:
Big Data and Machine Learning are already affecting our lives in both positive and negative ways,
propbably more than we realise.
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33. What's Next?
What's Next?

34. Human beings will never stop looking for improvements in
knowledge and technology, considering the value they provide to
us as a species. The policies of most governments and
corporations are universally directed towards growth and
automation, so it seems unlikely that the rate of progress and
innovation will slow down any time soon.
A summary of thoughts by Sam Harris
Neuroscientist and philosopher 💻
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35. As humans continue to develop increasingly intelligent machines, we will reach the
inevitable “singularity”. A future where:
🧠Machines will achieve an intelligence equal to or greater than that of the human being
🤖Machines will learn on their own and improve themselves without needing our input
The hypothesis...
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36. 🔺If we instil AI with human values, everything will be fine, philosopher Nick Bostrom
reassures us
🔻"The development of full artificial intelligence could spell the end of the human race" –
is the warning from Bill Gates, Steve Wozniak, and Stephen Hawking
...So which vision of this science fiction is correct?
The big question...
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What happens when the algorithms have made us obsolete?

37. 1.
2.
3.
AI models will serve as a mirror to realise the terrible errors and biases that our
behaviours are having. This will produce a deep reflection on humanity that will make
us a better society.
AI will sharpen and worsen our biases and errors, inevitably leading to chaos and
significant social deterioration.
The singularity will produce better machines than us and they will teach us how to do
things in a better way. Or they will decide to erase us because we have no solution...
Future predictions by the philosophy of Artificial Intelligence
In this new field, there are three main currents of thoughts:
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38. What do you think? Will artificial intelligence, machine learning, and data science make
our lives better or worse?
Is the future already written? Or can we still twist the course towards a better destination?
In Conclusion...
One way or another, we will find out soon enough!
Feel free to share your thoughts on LinkedIn – we'd love to
hear from you.
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📍Read the original article on Pangea.ai

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41. Thank you
Thank you
poly@pangea.ai | + 49 174 181 7323
poly@pangea.ai | + 49 174 181 7323 󾓨
󾓨 | +971 585094240
| +971 585094240
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