Blog

Big data and the outburst of machine learning

When Steve Jobs called the Vatican from a phone booth using a blue box to hack the analog phone system and avoid paying the fee, he may not have realized he was performing one of the first acts of hacking in history. Bill Gates was already another big computer star in the 196os, the computer age was dawning. Companies were slowly adopting computers to speed up and organize their business transactions, space management was one of the big sales pitches. Before personal computers, all documents had to be stored in warehouses, but with the invention of processing units, it became easier to facilitate the storage of large amounts of data in boxes as small as a microwave oven. Due to this new paradigm, the need arose to manage the information stored on hard disks and the solution was a special coding language to search and query the data stored in databases; this language is called SQL (Search & Query Language) and is still the main interpreter today. Today, SQL and databases are at the peak of their popularity and this is due to the large number of transactions that take place over the Internet.

With big data comes big responsibility. Over the last 15 years, artificial intelligence and machine learning have seeped from the academic community into our daily lives. There are already a large number of algorithms running on the backend of the websites and applications we use every day, from bots that decide whether an email we receive is spam or not, to aggregation services that collect personal information to deliver personalized ads to us. These programming technologies are only in their first period of development, but we can already glimpse what the future is going to look like based on the momentum that AI and Machine Learning are gaining.

Can we help? These are certain times of opportunity for the new generations. Software engineering careers are at their peak and whoever decides to get involved with these trendy technologies is going to discover that there is no limit to their curiosity, creativity, and professional development.

What can we do to start getting into computer science?

First of all, we need a good background in software development and hands-on experience in the specific areas of data analysis and manipulation; SQL is a must.

Machine learning is a methodology for teaching machines about our world so that they can perform tasks that we humans can do, but not as quickly and efficiently. Computer engineers teach machines by providing them with thousands of alternatives that a machine can find when processing a task and, through complex mathematical algorithms that function as the computer’s brain, the computer can sort the model of alternatives so that it can predict the results to provide a solution. While it would take us, humans, a lifetime to analyze all the songs uploaded to Spotify to provide suggestions, a machine can accomplish this in a fraction of the time. To obtain a result, the processing unit will use the set of examples and logic algorithms to analyze the data, the machine can also generate new examples by comparing the previously loaded set with new types of data collected and thus generate new solving paradigms.

There are different ways for a machine to learn to discover our world, one of them is called Deep Learning or Deep Neural Networks. These are logical algorithms programmed so that machines provide solutions through a set of hierarchical procedures and layers of abstraction. In this case, the machine mimics the functioning of the human brain to analyze the data and provide matching resolutions.

Another technique is called Reinforcement Learning, the difference between Deep Learning and this alternative is that in this scenario the machine learns dynamically by adjusting processes based on continuous feedback and applying the new learnings to new data sets. In other words, the AI waits for new information to discover new ways to solve a problem.

If AI and Machine Learning appeal to you and you want to get deeper into computer science, at RaceHub, we offer weekly after-school programs. The programs are catered to each student and can begin as early as the foundational level all the way to a more advanced, expert level. Our programs are for students of any age (starting as early as age 5) and all skill levels and progress through a variety of languages from Scratch to Python, Javascript & Java, to the fundamentals of Robotics and Engineering, to the latest trends seen through Artificial Intelligence (AI) and more. We also offer tracks of technology and business courses for families and adults.

Essential Learning

We offer weekly after-school programs. From Scratch to Python, Java & Javascript, to the fundamentals of Robotics and Engineering, to the latest trends seen through Artificial Intelligence and more.

Our programs are for students of any age and all skill levels. We offer tracks of technology and business courses for families and adults.The programs are catered to each student and can begin as early as the foundational level all the way to a more advanced, expert level.

The programs are catered to each student and can begin as early as the foundational level all the way to a more advanced, expert level.

At Race Hub, students gain the technical expertise, cognitive intelligence, and develop real world skills to become well rounded leaders of tomorrow.

Sign up for a free class!