How Machine Learning Is Changing The Game For Major Industries

The Age Of Machine Learning Techniques

Technology thought leaders like Elon Musk and Bill Gates point to artificial intelligence as one of the most important technologies for the future. Machine learning is a crucial aspect of artificial intelligence and an area where significant research is being pursued all around the world.

What A Machine Learning Developer Does

Behind all the successful machine learning projects, whether Google’s self-driving car or the smart media systems like Amazon Echo, you will find machine intelligence developers. The role of a machine learning developer comprises three essential clusters of skills:

• Deep statistical knowledge
• Knowledge of suitable programming languages for artificial intelligence algorithms
• Expertise with machine learning models

Machine Learning Languages

The knowledge of programming languages and techniques is critical in this new field. A profile for a machine learning developer working in the field would need to be very comfortable with a programming language such as Python, Java or C++. Many machine intelligence developers prefer Python for its ease of use. Advanced, “deep learning” systems and real-time systems that need the speed of processing will favor C++ instead. Newer programming languages like Julia promise both the ease of use of Python, as well as the speed optimization of C++.

Types Of Machine Learning

There are several types of computer intelligence, which all share the same broad scientific principles, such as an application of statistical inference and classification. As the chart below shows, the techniques are being applied in a wide range of industries. Data from Siemens shows that specific areas such as autonomous cars and smart assistants are growing very rapidly.

CHART: Fastest Growing Applications Of Computer Intelligence

Supervised Learning

The first type of computer intelligence is also the simplest. Supervised learning involves machine learning algorithms that apply statistical learning techniques to data that a developer labels beforehand. The algorithm then makes predictions on new data sets based on patterns around the labels that were provided. This results in highly accurate learning behavior.

Unsupervised Learning

This type of machine intelligence is slightly more complex. It involves feeding the system with unstructured data and letting the machine infer patterns in the data in the absence of any particular labels.

It is “unsupervised” in the sense that less guidance is provided to the machine in terms of what variables to focus on. There might be, say, 10 variables in a dataset. Whereas supervised learning would label the variables and train the algorithm based on, say, two variables, in unsupervised learning, the learning is more open-ended.

The machine can identify patterns based on variables whether they are considered, by the developer, to be relevant or irrelevant. While it requires more effort to get useful results, sometimes this type of computer intelligence can yield powerful, unexpected insights that solve complex problems.

Algorithms And Statistical Learning Techniques

Besides these basics, there is also reinforcement learning, as well as specific machine learning tools for solving classes of problems. Some examples include:

• Deep Learning
• Neural Networks
• Decision Trees
• Clustering
• Regression Models

Each of these features various types of machine learning algorithms that can be applied in a supervised or unsupervised learning environment.

Machine Learning In Healthcare

The healthcare industry is among the fields in which machine knowledge processing holds the most promise. According to Forbes, academics such as those at New York University’s Courant Institute of Mathematical Sciences are making important contributions to the industry.

These experts use technological advancements in computer intelligence to improve the way in which disease is diagnosed and treated. The machine intelligence specialists collaborate with medical teams from the likes of NYU Langone Medical Center and Beth Israel Deaconess Medical Center.

There are rich opportunities for startups in the medical field. Sentient.ai, AiCure, and Ayasdi are some of the startups that have received significant venture funding to apply computer intelligence in healthcare.

Artificial Intelligence In The Mobility Industry

The driverless car industry is the most prominent user of computer intelligence in the car market. This market has several competing entities that are all working on software to make cars drive better using autonomous systems. Large companies and deep-pocketed startups have zeroed in on this industry. A partial list of players in this field includes:

• Uber
• Google
• Tesla
• Magna
• General Motors
• Lyft
• Ford
• Aurora Innovation
• Volkswagen

According to The New York Times, self-driving cars are applying neural networks to solve problems like computer vision and pedestrian-identification.

How Smart Machines Are Changing Finance

In finance, machine intelligence is being used to replace human decision making in investments. Forbes points to so-called “quant” hedge funds like Renaissance Technologies for being the most successful at applying the new statistical learning techniques in capital allocation. Other financial institutions on Wall Street have developed highly sophisticated algorithms that apply artificial intelligence to investments.

Artificial Intelligence In Robotics

A variety of startups are actively developing smart robots that replicate human intelligence in industrial settings or in the home. RobotIQ lists some of the ground-breaking startups in robotics, which include:

• Velodyne LiDAR – This startup got $150 million in funding and makes smart robotic sensors to enable machine vision.
• Carbon 3D – This startup focuses on the use of innovative Continuous Liquid Interface Production in 3D printing to create better materials. It got $81 million in funding.

The Next Five Years Of Smart Machines

While much progress has been made in applying artificial intelligence to the real world, there is more to come. Technologists armed with smart algorithms and subject-matter experts are combining to create innovative solutions in every field imaginable. Everything from farming to pharmaceuticals and cybersecurity is fair game. VC firms, spotting an opportunity, will continue to back bold and audacious upstarts tackling gigantic problems. In the next five years, expect to see even more machine learning in action to change the way you live and get everything done.

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