Artificial intelligence, machine learning and big data

Artificial intelligence (HE), Machine Learning and Big Data are the great catalysts for change in these times. How deep and profitable those transformations are will depend on how much we dare to innovate and ride a wave that will otherwise simply continue..

Why Big data?

Big Data is rapidly moving into a new stage of maturity that promises even greater business impact, as well as a disruption in the industry in the coming years. As initiatives mature, Organizations now combine the agility of Big Data processes with the scalability of artificial intelligence. These two features help accelerate the delivery of value even more than before..

Secondly, the ability to manage large volumes and data sources is enabling the capabilities of AI and, especially, of machine learning, who have been sleeping for decades due to three relatively common problems:

1. The lack of data availability suffered by companies.
2. Limited sample sizes, that negatively affected the capabilities of organizations.
3. The inability to analyze massive amounts of data in milliseconds, due to lack of suitable tools.

Artificial intelligence and data explosion

Every year, the amount of data we produce doubles. IDC forecasts say that in the next decade there will be 150 1 billion sensors connected to the network (more of 20 times the Earth's population). This data helps artificial intelligence devices learn how humans think and feel.. Accelerate your learning curve and also enable automation of data analysis. The more information there is to process, more data the system receives, more learn and, as a last resort, the more accurate it becomes.

Today, artificial intelligence is able to learn without human support through machine learning or machine learning. New cases are known every day, like Google's DeepMind algorithm, who recently taught himself how to win 49 Atari games, without requiring any kind of interaction from people.

In the past, AI growth was minimal for two main reasons:

1. It was based on a limited data set using representative samples rather than using real data in real time.
2. The inability to analyze massive amounts of data in seconds.

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Artificial intelligence, powered by Big Data

Increasingly, Companies from all sectors join Artificial Intelligence pioneers such as Google and Amazon to implement AI solutions in their organizations..

MetLife, one of the world's largest providers of insurance and employee benefit programs, has also promoted artificial intelligence initiatives with Big Data. Your project of artificial intelligence is based on voice recognition to achieve a better vision of the business thanks to the monitoring of incidents and results.

The company has more efficient claims processing, where commonly used models have been enriched with unstructured data that is now parsed, like medical reports. Thus, when analyzing each interaction new factors come into play, complementary information that adds value and allows you to discover important details.

But, Can a computer think like a human brain?

Some say never, while others say that is already happening. Following the news and decisions of large technology companies like Facebook lets us know that, despite some shortcomings And apart from the scandals, robotic interventions can seems moved by reason in some cases.

The truth is that we are at a point where the ability of machines to see, understanding and interacting with the world is growing at a tremendous rate, that continues to grow with the volume of data that helps them learn and understand. faster. Big data is the fuel that powers AI artificial intelligence.

Three fundamental ways big data is powering artificial intelligence

There is, in particular, three ways big data enables businesses to grow. artificial intelligence offering opportunities for algorithms that know how to take advantage of:

• Big Data Technology: now, companies and their artificial intelligence projects have the ability to access large volumes of information. From there, are in a position to process large amounts of data that previously required extremely expensive hardware and software.
• Availability of large data sets: ICR (Information gathering request), transcription, voice and image files, Weather data and logistics data are now available in ways never before possible. Today, even old files “originated on paper” are also available in digital format.
• Machine learning at scale: Algorithms “scaled”, such as recurrent neural networks and the deep learningDeep learning, A subdiscipline of artificial intelligence, relies on artificial neural networks to analyze and process large volumes of data. This technique allows machines to learn patterns and perform complex tasks, such as speech recognition and computer vision. Its ability to continuously improve as more data is provided to it makes it a key tool in various industries, from health..., are driving the advancement of AI. Artificial intelligence.

Artificial intelligence, especially machine learning combined with Big Data and data management, provides companies with the opportunity to gain expertise analyticsAnalytics refers to the process of collecting, Measure and analyze data to gain valuable insights that facilitate decision-making. In various fields, like business, Health and sport, Analytics Can Identify Patterns and Trends, Optimize processes and improve results. The use of advanced tools and statistical techniques is essential to transform data into applicable and strategic knowledge...., achieve predictive models and make real-time decisions on a universe of data invaluable to the human mind.

The tools are. The possibilities exist. It is up to each organization and its managers to decide whether to ride the wave or not.