How Could Machines Learn Themselves?

The Basics of Machine Learning:

Holistically, machine learning can be broken down in two major types: supervised and unsupervised learning. Traditionally, machines have always performed well above expectations for supervised learning as this involves known inputs and outputs overseen by humans. Machines can also be developing unsupervised learning where machines predict outcomes which never happened before from humans. This way of machine learning will ultimately boost efficiency and productivity.

Reinforcement learning is a subfield of machine learning. Unlike supervised and unsupervised learning, it follows trial-error process to find out target state to get reward. Machines usually work in certain environment to perform series of actions in sequential order to learn from mistake and rewards.

Supervised learning can be combined with reinforcement learning where goal is set and actions to reach goal are unknown. Humans can involve this learning process by providing constant feedbacks to the machines to perform ‘learn and improve’ process. This is hybrid version of machine learning will be more flexible to develop better machines learning concept.

Why is machine learning so important?

Machine learning algorithms are usually on-the-shelf products that you can take and apply to your appropriate business problems, such as classification, linear regression and clustering problems. This step may involve trying more than one machine learning methods since we don’t know which methods can fix our problem under business requirements. Building working model is essential step to execute machine learning algorithms. Like many pioneering ideas, it will evolve many trials and revolution in order to come up suitable model that can make machine learning model to become such important component of running engine that each organisation needs to have.

With increasing productivity and development in modern technology in today’s business environment, businesses today use machines for automating existing business processes in order to maintain price-competitiveness and efficiency in industry. With more and more autonomous systems applied in businesses, naturally labour costs and profit will rise.

How can we make machines learn themselves?

Whilst machines are good at processing big data and making quantitative decisions relative to the human brain, it lacks “common sense” and the ability to dissect and learn from mistakes. In order to develop this common sense in machines, programmers need to create and supply machine systems with as many user cases or scenarios as possible to expose the machine systems these data sets; allowing it to then let it to grow and learn from its past experience. This process of data immersion will allow programmers to develop machines to think like a human, ultimately, improving productivity and efficiency in every industry.

Like humans, machines need to constantly improve and grow new areas of expertise and skills. Just as humans want to boost their careers to the next level, machines similarly seek to constantly learn new skills and creative challenges outside of comfort zones in order to get better reward and bonus etc. Thus, it is for this precise reason that machine learning - specifically unsupervised learning - is fundamental for efficiency and productivity of information systems. 

In the future, communication networks between machines will be essential for collaboration and expediated learning between machine systems just like humans would learn from each other’s mistakes. Facilitating this in the future between machines will be essential for the development of AI networks and is expected to work in a very similar way to how humans organise networking activities today. Although these indeed seem like daunting tasks for humans to facilitate, ultimately these are all essential to develop their own kind of ‘the common sense’ in machine systems.

How can we design machines learning?

There are a few important factors needed to be considered:

1.   Does it save human resources lots?

2.   Can machine learning perform task more efficiently and effectively?

3.   Can machine learning expedite human’s knowledge?

When architects design the solutions, they need to address these questions and hence use suitable learning types to fit the different industries for their needs.

Scoring these factoring and give the final score for each factor will determine the final recommendation on each learning type.

How can we put it altogether to produce machine learning product?

Building machine learning product in your company will help you to achieve more for less efforts. Company needs to come up a business plan for machine learning projects.

Here is the example of a business plan for the machine learning project:

Company will need solid transformation plan to turn into ML company. Here are recommended steps:

1.   Setup pilot project to run as momentum for ML.

2.   Create ML team.

3.   Conduct internal ML training.

4.   Extend ML in future roadmap for company strategic.

5.   Develop internal and external communications.          

Thanks for reading this, please leave comments if you like to add. Any advices or suggestions would be appreciated.