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A Glimpse Into the Future of Data Science - What's Next for AI, Big Data & Machine Learning?

  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 @ 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 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 🌍 “
  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...
  6. How did we get here? How did we get here?
  7. Bayes' Conditional Probability Theorem - 18th century Data science theories have existed for hundreds of years:
  8. Gaussian Distribution and Markov Chains - 19th century Data science theories have existed for hundreds of years:
  9. Pearson Correlation Coefficient - 20th century 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.
  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 ▶
  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.
  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.
  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?
  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. 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?
  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
  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. 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?
  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.
  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 💻
  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...
  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... 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:
  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. 📍Read the original article on
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