Future problems that Machine learning can solve

Machines have far superior computational abilities than humans. They can sort through enormous amounts of data and use it to make better decisions. So the general idea behind AI is to have machines do the heavy thinking for us.

Machine learning is a type of AI that allows computer programs to adjust when exposed to new data, in effect, “learning” without being explicitly programmed. Machine learning is similar to data mining in which databases are examined by humans to produce new information and insight.

Analyzing this complex data to derive meaningful value is often overwhelming, inhibiting our ability to find adequate solutions in a timely manner.

Artificial intelligence will eventually touch nearly every industry on the planet. Here are couple of ways AI can help transform certain industries.

Cyber Security

Machine learning predictive analytics provides a powerful use case for network and cybersecurity applications. Organizations today are inundated with myriads of network connections and traffic flows, as well as cyber security eventsthat require analysis and potentially, remediation. The sheer volume of traffic and events as well as the complexity of today’s hybrid cloud networks makes it impractical to have human beings attempting to analyze all the network and cybersecurity data being collected and making decisions based on this data. AI prediction technology looks at millions of files and attacks to learn exactly what makes them up. By understanding this mathematical DNA, they can prevent and protect against future attacks.

Over time, billions of files have been created — both malicious and benign. In the file creation evolution, patterns have emerged, reflecting how specific types are constructed. Variability and anomalies exist, but generally the computer science process is reasonably consistent.

The patterns become even more consistent across development shops such as Microsoft, Adobe and other large software vendors. That consistency increases as one looks at development processes used by specific developers and attackers. The challenge lies in identifying patterns, understanding how they are manifest and recognizing what consistent patterns tell us about the nature of these files.

Currently, there’s a lack of enough qualified and experienced cyber security analysts to help minimize the skyrocketing global cyber-attacks. And, there already exists an overabundance of big data that can be used in several algorithms to improve the current state of cybersecurity with ML. Let’s all hope that these research developments will help drive new methods that will boost current state-of-the-art in both ML and cybersecurity.

Reducing Energy Costs

Energy sector can use artificial intelligence to sift through vast datasets to predict and adapt to certain scenarios. They can reduce operational costs and mitigate issues proactively.The problem was the data was too complex for traditional formula-based engineering and human intuition. Each data center had a unique architecture and environment that required a custom-tuned model. A model for one system may not be applicable to another. Therefore, a general intelligence framework was needed to understand the data center’s interactions.

Employees can now monitor the temperature and performance of production machinery in real-time, and react immediately to solve problems. Companies will be able to optimize heating, ventilation and air conditioning (HVAC) usage and analyze patterns to deploy energy-efficient lighting systems. It becomes easier for organizations to monitor smart equipment to identify non-performing ones and fix the issue in an efficient manner.

Energy data analytics opens new doors to identify areas of inefficiency and implement targeted energy-saving initiatives. Using machine learning and big data analytics for energy efficiency can help address critical challenges in quality, productivity and efficiency.

A consistent challenge with renewable energy sources such as wind and solar power is their unreliability. Weather-dependent power sources will often fluctuate in their strength. Energy providers are implementing AI in an attempt to address these challenges. This meant that greater precautions could be taken to harness and preserve the energy that was generated. In order to provide these detailed weather reports, the AI system mines a combination of data from local satellite reports, weather stations as well as wind farms in the surrounding area. The algorithms driving the system are trained to identify patterns within these data sets and make predictions based on those data points.

Google has used its artificial intelligence platform Deep Mind to predict when its data centres will get too hot. Cooling systems are only activated when required. AI has saved Google around 40% in energy costs at its server farms.

Hopefully in near future Organizations can intelligently increase the efficiency of entire machine parks and save energy costs at the same time with the help of AI and machine learning.

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