What is machine learning?

AI systems generally have the ability to plan, learn, reason, problem solve, perceive, move and even manipulate. Machine learning is one of the many approaches being used in AI systems. Others include evolutionary computation, and expert systems.

Machine learning is a part of many things that we do every day. Think about where machine learning systems might influence your life:

• Recommendation systems on your favorite streaming services like Netflix or Spotify are run by machine learning.
• Search engines use machine learning to clarify and optimize your search results.
• Social media channels recommend friends, groups and videos to watch.
• If you have a modern fridge, often these learn when you use it the most and cool it in anticipation of dinner time.
• GPS anticipates what parts of your route will have heavy traffic and re-routes you using machine learning algorithms.
• Voice-based assistants like Alexa and Siri use machine learning to operate.

Each of these platforms amass data from the everyday choices you make. It learns about you, and from the information gained it makes predictions about what you will watch next, what time you’ll make dinner, or where you might travel or buy.

All of this data powers the machine learning algorithms, which then will help a brand anticipate what you may want to do or buy next. Not only that, but your likes and dislikes are combined with other data points from millions of other people, allowing companies to create accurate and highly effective suggestion lists.

AI is poised to scale newer heights using machine learning applications.

Applications of machine learning

The applications of machine learning are vast. Here is a look at how it is being used in key areas that are integral to everyday human life.

Machine learning in education

When applied in the field of education, machine learning can help teachers examine the type of lessons students can consume. They can evaluate how students are managing with lessons taught – how much they are able to grasp, what are the common topics that the students tend to struggle with, and what’s too easy. This helps teachers to better plan lessons, and identify students who may be falling behind, allowing far more effective interactions and interventions.

Machine learning in search engines

When you type a search term into Google, it’s frustrating when the results that come up aren’t what you’re looking for. Machine learning has been an integral part of search engine optimization for a long time now. It is constantly helping search engines show more relevant results to searches. It has also helped power voice-based search services, image searches and several other search related features.

Machine learning in digital marketing

Personalization is the key to modern digital marketing campaigns and machine learning has been integral in achieving this. With data based on consumer interactions, machine learning has helped companies personalize their approaches to potential customers, focusing the right messaging at the right time. From personalized emails, to cross or upselling based on recent purchases, machine learning has helped businesses leverage their data on consumer behavior.

Machine learning in health care

Machine learning has been extensively applied in the medical field. Diagnosis using medical imaging is an important example where machine learning works with diagnostic tools. Machine learning views the medical images, identifies areas that are unusual or abnormal, doing so without any bias that a medical professional may have.

Machine learning is also being used to help doctors in treating unique cases of specific illnesses by providing them with suggestions on treatment protocols based on information gathered from other cases. For instance, a library of macrophages can be trawled in hours by machines that identify likely efficacious phages to treat strains of antibiotic resistant bacteria.

The application is also experimenting with how to convert pooled consumer data gathered from personal devices to provide medical professionals with suggestions and options on treatment. This is of course a sector that is constantly evolving.

The applications for machine learning are diverse and can be found in just about any field or kind of business. The benefits for commercial, government, and social ventures are immense.

Benefits of machine learning

Machine learning has incredibly wide ranging benefits across almost every facet of life. These are just some of the universal benefits of machine learning:

Predicting customer behavior

Analyses of consumer purchase patterns helps give companies insight into the way forward for product and service lines. These patterns can be as precise as why a customer may opt for one product over another, the influences of pricing, season, brand loyalty and more on these decisions. Such data-oriented findings are made much faster with machine learning and speed is the key to smarter decision-making.

Sustained accuracy in data entry

The most boring of human tasks is that of data entry. The chances of an error are high with such repetitive tasks. These errors can prove costly to a company on several levels. Machine learning ensures that data entry is completed quickly, with precision, leaving no room for error. It also takes mundane tasks away from employees allowing them to concentrate on more challenging and business beneficial jobs.

Discovering leads in user experiences

Every business grows on the basis of new leads that convert to paying customers. Being able to stay at the top of your game is about evolving to meet the needs of the customer. Machine learning helps businesses by diving into customer journeys and providing insights into trends and anticipating needs. Research has shown that machine learning has made a difference to the upward growth trajectory of businesses by helping them to predict customer behaviors, find inefficiencies, etc.

Maintaining a competitive edge

Businesses are able to grow alongside the market when they have good business intelligence to fall back on. Machine learning has an important role to play here in providing businesses with insights on their unique selling points and its positive aspects in comparison to competing brands. Any new approach can be quickly hypothesized, tested based on available data and help businesses build a go-to-market plan quickly.

Powering virtual assistants

Workplaces, big or small, are about increasing efficiency and making smart use of worker hours. Machine larning, when applied to automatic speech training, helps create smarter and more efficient virtual assistants, who can take down notes, develop minutes of meetings and maintain better records. All this reduces mundane paperwork that is essential but tiring to do. With better virtual assistants, precision is ensured and privacy regulations are well met.

Categorizations of machine learning algorithms

Algorithms form the basis of machine learning’s entire structure and its growth. These algorithms can be divided into four main categories:

Supervised machine learning algorithms

Here, lessons learned earlier can be applied to new data with the help of labeled examples to predict future outcomes. This begins with the analysis of known training datasets. The learning algorithm creates an inferred function which will make predictions of possible outcomes. With the necessary amount of training, all new data inputs will be provided with targets.

Unsupervised machine learning algorithms

These are in contrast to supervised algorithms and come into play when the training information is not labeled or classified in any way. Unsupervised learning does not provide ‘correct’ outputs for new data. Instead these algorithms explore the data, draw inferences from datasets and reveal any hidden structures that might be in unlabeled data.

Semi-supervised machine learning algorithms

These algorithms follow the middle line between the first two types, because of the use of both labeled and unlabeled data for training. Typically, the amount of unlabelled data is larger than the amount of labelled data and the algorithm uses the labeled data to learn about the unlabelled data. Systems based on this constantly improve on the level of accuracy of learning.

Reinforcement machine learning algorithms

This is a learning method where interaction with the environment produces actions and uncovers errors and rewards. With this approach, machines and all software agents are able to determine the appropriate behavior within a specific context for the best performance possible.

The challenges of machine learning

Despite all the leaps forward in technology, there are still a range of challenges that machine learning needs to overcome.

Networks still need huge amounts of working memory to store and process data. While some unsupervised learning techniques remove unneeded data, there is still a need for massive processing power. This can be partially resolved, with unsupervised learning algorithms stripping unneeded and excess data which cuts back on processing power needed. However, this is not enough for all scenarios.

Natural language processing is still a long way off from being a natural and accurate translation. Slang, accents and understanding of language are still huge challenges for machine learning. While the machine constantly has new data to listen to and learn from, it still needs a lot of training to resolve more obscure accents.

AI washing is when technology is labelled as artificial intelligence (or an intelligent computer), when it’s actually just machine learning or the same old algorithms they have always used. For many people, the distinction isn’t important, but it over-inflates technology expectations, undermines trust in technology and sets up both fields for backlash. Education of the general public and more understanding of AI and machine learning are needed.

Lack of video training is holding back the industry. Instead of relying on static images and a 2D world, video provides much richer datasets. Our world is dynamic, and our machines need to learn that. This is an emerging field of study.

Machines don’t think like humans. People use heuristics to make snap decisions. They use a broad field of attention to integrate a holistic understanding of a scene. But machine learning is still about granular data, which limits the current ways it can be effectively used. As machines learn more, this will resolve, but it’s unknown if they will ever truly think like humans or become "artificially intelligent."

The future of machine learning

As machine learning programs and data science techniques become more widely available, there are huge benefits for almost every facet of life.

• Fine-tuned personalization: Will empower businesses to anticipate and cater to customer needs.
• Better search engine experiences: Improved ranking of search engine results help both end users as well as admins in delivering pin-pointed results and insight.
• Evolution of data teams: Everyday data and IT team roles will evolve with improved machine learning, reducing the amount of time spent on manual programming. For instance, data scientists can spend less time cleansing the data as machine learning learns to do it effectively (through the use of AutoML).
• Rise of quantum computing: It may sound like something from a sci-fi film, but quantum algorithms do carry the potential to lead to multiple other innovations and it is something that will happen in the mid to long term.

Artificial intelligence and machine learning are poised to change the way the world does business, provides governance, and develops new technology. It will change the way application-development markets function in the future. Together these technologies have been accorded the importance given to electricity at the start of the industrial revolution. These two together herald a new era in information technology.

AutoML

AutoML is exciting new technology that means ordinary people can now run complex machine learning processes. In the past, data scientists have needed an in-depth understanding of statistics, data cleansing techniques, computer coding, algorithms and also access to powerful computers. This has meant that for most people, machine learning was out of reach.

New software being developed has changed machine learning. Online software programs take data uploaded by a user. The user identifies what kind of predictions they need, and the software chooses the correct algorithm to run, and produces a set of clear, concise and explainable results. While the predictions still require data to be accurate and labelled, there are also data cleansing techniques built into the software. They can assess outliers and missing information, often building strategies to manage the discrepancies as they go.

This is truly a window into the future for companies wanting the ability to make predictions and process data that don’t have the facilities or means to hire dedicated data scientists. For now, data scientists have mostly been taking advantage of AutoML’s data cleaning abilities which has saved a lot of time.