The Most Famous Data Scientists in the World Today and How They are Using Their Data Science Methods


Data Science, like man-made and machine-created data, is a discipline that has developed by leaps and bounds. It has increased the number of people from other professions, such as mathematics and biology, using data to solve issues. Algorithms now handle much more than just numbers and letters. They can now handle practically any data format, including photos, movies, and audio. Companies now have access to a broader range of unstructured data. Data sources have expanded as well, and social media data is now one of the most important sources for many businesses looking to profile individuals. All of this is on top of the structured data that is currently rising at an exponential rate.
The Most Famous Scientists that Worked are:

Massive breakthroughs have been made in data science, and we should expect more in the coming days. We are at a point in time when groundbreaking data science discoveries are being made and applied to real-world challenges. It's worth taking a look back at some of the most significant discoveries and findings from the beginning.

Alan Turing

Alan Turing is perhaps one of the most well-known data scientists ever. He is widely regarded as the founder of both artificial intelligence and theoretical computer science.

He rose to prominence as a result of the film "The Imitation Game." His development of Bombe, the electromechanical device used to crack Enigma (the German cipher technology from World War II), was not, however, his sole breakthrough. His study resulted in the development of the first machine capable of calculating whole mathematical situations. The machine's pilot model featured a clock speed of 1MHz, making it the fastest computer at the time. His study was also utilized to determine aircraft movements during the Cold War.

He also devised the Turing Test, a set of guidelines for determining whether or not a machine can think and act in the same way as a person. The pass percentage is determined by how well a machine can impersonate a human. Today, we utilize a variety of tests, the most common of which is Captcha. Captcha is a type of reverse Turing test in which people must verify they are not machines.

Alex Krizhevsky

The year 2012 was important in terms of deep learning (a branch of machine learning where artificial neural networks are used to extract features from big data). Krizhevsky increased the power of neural networks to previously unheard-of levels. He created "Alexnet," an algorithm that cut the ImageNet competition's mistake rates in half (almost 15%). Individuals must categorize millions of items across hundreds of categories in the ImageNet Challenge.

His program could recognize cats with an accuracy of over 75% and faces from YouTube videos with an accuracy of over 80%. This individual is responsible for facial recognition software that is used in security systems and that you use to unlock your phone today. Another sector that has benefited greatly from the internet is medical imaging.

Ian GoodFellow

Generative Adversarial Networks (GANs) were first brought to the world by Ian Goodfellow, and they can have two sorts of models–

Once trained on data, the generator model attempts to generate new instances of the same type.
The discriminator model attempts to distinguish between genuine and fraudulent (created) material.

Regrettably, the generator model has been severely misused in recent years, most notably in the DeepFakes. Many people have posted implausible remarks of famous people on the internet, which were later discovered to be DeepFakes. It's created a bag of worms in which nearly anyone with a laptop and an internet connection may clone an existing video and make the speaker say anything they want. The artificial intelligence at work learns from an existing video and can then duplicate the facial expressions, voice, and speaking style automatically.

The algorithm has encroached on human creativity in a way that no other machine code has before. It can make paintings and construct (non-existent) faces. GAN paintings have even fetched as high as $400K at auctions. Since the problem has gotten out of hand, companies like Adobe have developed new algorithms to detect bogus information. GANs have not only affected the current AI environment but they are also expected to lead to more dramatic breakthroughs in the next years.

Sebastian Thrun

The algorithm has encroached on human creativity in a way that no other machine code has before. It can make paintings and construct (non-existent) faces. GAN paintings have even fetched as high as $400K at auctions. Since the problem has gotten out of hand, companies like Adobe have developed new algorithms to detect bogus information. GANs have not only affected the current AI environment but they are also expected to lead to more dramatic breakthroughs in the next years.

Andrew Ng

The open-source community, as well as data scientists like Andrew Ng (the cofounder of Coursera), have made significant contributions to making Data Science accessible to the general public. In 2015, Google offered TensorFlow free to use, and in 2016, Facebook did the same with PyTorch. Custom Python libraries (such as Scikit Learn and Pandas) have made it quite simple for anybody to get started in a few of hours.

Individuals without a mathematical background have benefited from courses like Andrew's in learning how AI algorithm’s function. Websites like Kaggle and GitHub have also made AI tasks, datasets, and solutions readily available to anybody with an internet connection.
Data Processing using Cloud Infrastructure

With each passing year, the amount of data collected has increased. Third-party sources and social media data have been contributed by companies. Cleaning, normalizing, processing, and formatting such large datasets, on the other hand, is a difficulty. Processing semi- or unstructured data, which many of these sources create, necessitates greater resources. On local workstations, even running algorithms on test data might be a huge difficulty (laptops).

This is why cloud service companies like Amazon Web Services (AWS) have seen their revenues soar to billions of dollars. AWS S3 is an example of a cloud service that offers exceptionally low-cost data storage. These are also some of the very first cloud services to appear. Data storage is hardly the beginning; processing and formatting services have also grown in popularity. Data engineers, as opposed to data scientists, are in more demand nowadays. They can compute and build an effective architecture for data-driven systems.

All of this has had an impact on how businesses employ big data and cloud services. Providers of DaaS (Data as a Service), such as iWeb Scraping, offer data as a service. Companies can use these services to get access to third-party or competitor data by identifying the websites from which they want data scraped and the data points they need.
Internet of Things

While the Internet of Things is not a new concept, it is only now that a growing number of actual gadgets are communicating with one another. More gadgets than ever before are connected to the cloud, and they are collecting and sharing all of the data acquired by their sensors.
Natural Language Processing that is More Powerful

NLP is a subset of Artificial Intelligence that focuses on human language. It is the source of Siri's or Alexa's strength. Instead of focusing just on grammatical production, it examines how languages are utilized in real-time. Companies are anticipated to incorporate the newest NLP results into future products to make it easier for people to connect with computers and software. We're not far from a day when you'll be able to speak to your computer and have it complete work for you.

Medical science has been greatly affected by machine learning and data science. We've used it to solve challenges like detecting diabetes, identifying cancer cells, radiography, and pathology. According to a Stanford research, AI can detect skin cancer just as effectively as physicians can.

Many of the study findings and articles will be put to practical use in the following decade. Several breakthroughs are possible–

Disease detection and prediction, even before they occur.
Medical photos might be processed more quickly by machines than by humans.
Detecting and predicting epidemics, such as COVID-19.
Smarter Health data and tracking via a variety of devices, including smartwatches.

The distance we've travelled is enormous! Today, we can execute computations that would require computers the size of a toenail on a chip the size of a toenail. Data science and its real-world applications have benefited from advancements in chip production, as well as improved internet and data transport speeds. Data science's future will be determined by a variety of industries and organizations, and democratic data science will establish a fair playing field for all.

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