Get a good basic understanding of Ai & Robotics, without being into software development
Learn how the World is going to be different and how we could make it the best ever with good Ethics
Understand the full power of Ai with simple examples
Understand the surprising Robotics production growth model
No programming skills needed. I use everyday language and simple concepts.
We are at the extremely special point in time where humanity transitions from biological intelligence to digital intelligence and a robotic workforce. I will go through what it will mean to our daily lives, the economy, jobs, income, production and property law. All done with a positive attitude but also a healthy sense of caution.
I have put in a lot of work creating this course and I truly hope it will help bring a useful and exciting perspective on the future.
At this point everyone should consider what the future could look like with A.i. and what can be done to get there the best way.
The neural network progress is substantial and this is a historic moment where we have to pay extra attention.
This is intended for everyone who cares about our future with Ai and Robotics. No programming skills needed. I use everyday language and simple concepts.
Each video includes small exercises and thought experiments to help expand your understanding of A.i. and Robotics.
Learn how the World is going to be different and how we could make it the best ever with good Ethics.
Understand the full power of Ai with simple examples.
Understand the surprising Robotics production growth model.
Take part in Q & A where you can get feedback on your thoughts and questions related to the course.
Who this course is for:
This is intended for everyone who cares about our future with Ai and Robotics
Possible applications of Machine learning in the industry
Benefits of Machine learning in the industry
The ‘machine learning’ is a part of artificial intelligence and consists in that machines learn from real data without being directly programmed for it. In this post we will see how to use the advantage that these algorithms can bring to the industry.
Machine learning is a branch of artificial intelligence (AI) that allows machines to learn through algorithms. These algorithms learn from real data with which a model is generated. This model allows predicting what class or what type is a new data.
Within machine learning we find two types: supervised learning and unsupervised learning.
In supervised learning the data must be correctly labeled with the class to which it belongs, it is necessary to have a dataset with labels.
In the case of unsupervised learning, the data is entered into the model without any type of reference regarding the class to which they belong, and it is the same algorithm that classifies these data based on their characteristics.
This type of machine learning algorithms allow to detect patterns and classify new data from the trained models. For example, they can be used to detect faults or make decisions without the need for human intervention, which opens up many possibilities to automate processes that were not possible until the appearance of this type of algorithm.
These are some of the necessaryphases to identify and carry out a project based on machine learning:
Data acquisition: images, numerical data, existing databases, etc. Large amounts of data are required.
Creation of the dataset from the data obtained. For the creation of the dataset it is necessary to carry out the labeling of all the data (supervised learning). Usually this task is done manually and is quite tedious.
Model training. The model is trained with part of the data from the dataset.
Evaluation of the model. To obtain the behavior of the model, it is evaluated with new data that have not been used during training.
Machine Learning vs Deep Learning
A few years ago a branch of machine learning emerged that is known as deep learning or Deep learning. Machine learning algorithms are based on regression equations and decision trees, among others. However, Deep learning algorithms use what are known as neural networks that in a way try to mimic the functioning of neurons in living organisms. They are a set of neurons connected to each other and that perform mathematical operations to extract parameters and characteristics, to finally obtain a classification result.
Deep learning in computer vision
Computervision combined with Deep learning allows solving more complex problems than traditional vision, using more robust algorithms based on the learning methods provided by Deep learning. With the advancement of this technology, problems can be addressed and solutions designed that until now were not feasible.
These types of applications are designed for complex and changing environments in which characteristics cannot be extracted with traditional algorithms. They are used in character recognition applications, inspection of surface defects, security applications among others.
Mainly, deep learning allows you to expand solutions that are limited to traditional vision applications.
Possible applications of Machine learning in the industry
Applications based on machine learning algorithms can be used in different sectors and to solve very different problems.
Quality systems: machine learning algorithms create models that allow, for example, to detect defects in parts. Surface type defects in manufacturing, painting, etc. They also allow quality checks in an assembly process, presence or absence of parts, inspect welds, etc.
Production: in production, vision systems and robotics are combined with machine learning algorithms to improve processes and increase productivity. It is possible to automate tasks with variability that a traditional robot could not carry out by itself: recognizing and locating types of parts, processes and variable paths, etc. This allows in many cases to reduce costs and increase the competitiveness of companies.
Machine maintenance and predictive maintenance: by analyzing data (of any type) obtained from the different machines, models can be generated that are capable of predicting when a failure will occur. This serves to improve processes and prevent failures before machines break down. Avoid downtime in production and reduce preventive maintenance times.
The ability of these machine learning algorithms to process a large number of data allows the processes to be monitored and all their parameters controlled, which avoids errors and failures and therefore increases the final quality of the product.
Benefits of Machine learning in the industry
As you have seen, the use of machine learning algorithms has many benefits. Systems that are based on this type of algorithm are more versatile and are capable of working in changing environments and adapting to them. You can perform tasks and solve problems related to computer vision, robotics and data analysis, among many others, which, until the appearance of these algorithms, was unthinkable. All this makes machine learning applications a great ally of Industry 4.0 when it comes to automating processes.
Some of the clear benefits that can be obtained from using these systems are:
Reduction rate of failure. They allow the detection of failures and their reduction, which has a direct impact on the quality of the process and its improvement. The mistakes that are made help improve the process.
Stock prediction. These systems also make it possible to prevent errors and failures. Models created from data are capable of predicting when an error will occur, which allows preventive actions to be taken so that it does not occur.
Process automation. With these algorithms, processes can be automated that would not be possible without learning-based systems: variable inspections, changing environments, etc.
Do you want to use applications based on machine learning in any of your Projects? Contact us!
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Many services that we use every day rely on machine learning – a field of science and a powerful technology that allows machines to learn from data and self-improve.
Machine learning is used in internet search engines, email filters to sort out spam, websites to make personalised recommendations, banking software to detect unusual transactions, and lots of apps on our phones such as voice recognition.
The technology has many more potential applications, some with higher stakes than others. Future developments could support the UK economy and will have a significant impact upon society. For example, machine learning could provide us with readily available ‘personal assistants’ to help manage our lives, it could dramatically improve the transport system through the use of autonomous vehicles; and the healthcare system, by improving disease diagnoses or personalising treatment. Machine learning could also be used for security applications, such as analysing email communications or internet usage. The implications of these and other applications of the technology need to be considered now and action taken to ensure uses will be beneficial to society.
Machine learning is distinct from, but overlaps with, some aspects of robotics (robots are an example of the hardware that can use machine learning algorithms, for instance to make robots autonomous) and artificial intelligence (AI) (a concept that doesn’t have an agreed definition; however machine learning is a way of achieving a degree of AI).
What is the Royal Society project about?
There are both opportunities and challenges around this transformative technology
There are both opportunities and challenges around this transformative technology and it raises social, legal, and ethical questions. This is why the Royal Society is starting a project on machine learning, aiming to stimulate a debate, to increase awareness and demonstrate the potential of machine learning and highlight the opportunities and challenges it presents. In the course of the project we will engage with policymakers, academia, industry and the wider public.
The project will focus on current and near-term (5-10 years) applications of machine learning. It will have a strong public engagement element, and a variety of resources will be produced over the course of the project. Details of these will also be posted these web pages.
The project scope was developed by a Core Group of experts who met over the summer 2015.
Who will inform this project?
This Royal Society project is led by a Working Group involving a range of expertise.
Answers to our call for evidence (now closed) also inform the project.
Evidence gathering sessions and public events will be held over the course of the project.
What will come out of the project?
The project also pulls together evidence-based recommendations in a policy report for UK and EU policy makers, published April 2017.