All about Artificial Intelligence !!!..
“We are not there yet, but we believe that this could be the basis of a speech prosthesis”
‘Joseph Makin’ de l’université de Californie, à San Francisco (Etats-Unis).
Introduction :
In the first half of the 20th century, science fiction familiarized the world with the concept of artificially intelligent robots. It began with the “heartless” Tinman from the Wizard of Oz and continued with the humanoid robot that impersonated Maria in Metropolis. By the 1950s, we had a generation of scientists, mathematicians, and philosophers with the concept of artificial intelligence (or AI) culturally assimilated in their minds. One such person was Alan Turing, a young British polymath who explored the mathematical possibility of artificial intelligence. Turing suggested that humans use available information as well as reason in order to solve problems and make decisions, so why can’t machines do the same thing? This was the logical framework of his 1950 paper, Computing Machinery and Intelligence in which he discussed how to build intelligent machines and how to test their intelligence.
1- Artificial Intelligence:
Artificial intelligence (AI) has received increased attention in recent years. Innovation, made possible through the Internet, has brought AI closer to our everyday lives. These advances, alongside an interest in the technology’s potential socio-economic and ethical impacts, bring AI to the forefront of many contemporary debates. Industry investments in AI are rapidly increasing, and governments are trying to understand what the technology could mean for their citizens.
The collection of “Big Data” and the expansion of the Internet of Things (IoT), has made a perfect environment for new AI applications and services to grow. Applications based on AI are already visible in healthcare diagnostics, targeted treatment, transportation, public safety, service robots, education, and entertainment, but will be applied in more fields in the coming years. Together with the Internet, AI changes the way we experience the world and has the potential to be a new engine for economic growth.
The ultimate goal of AI is to produce revolutionary new knowledge to solve our biggest problems.
2- Machine learning:
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide.
For that, the primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
Check the video below:
3-Deep Learning:
Deep learning or deep learning is a type of artificial intelligence derived from machine learning (machine learning) where the machine is able to learn by itself, unlike programming where it is content to execute the rules to the letter predetermined.
Check the video below:
A quick overview of the 3 precedent concepts
Conventional Programming vs Machine Learning
Traditional Programming
Traditional programming is a manual process — meaning a person (programmer) creates the program. But without anyone programming the logic, one has to manually formulate or code rules.
In machine learning, on the other hand, the algorithm automatically formulates the rules from the data.
Machine Learning
Unlike traditional programming, machine learning is an automated process. It can increase the value of your embedded analytics in many areas, including data prep, natural language interfaces, automatic outlier detection, recommendations, and causality and significance detection. All of these features help speed user insights and reduce decision bias.
For example, if you feed in customer demographics and transactions as input data and use historical customer churn rates as your output data, the algorithm will formulate a program that can predict if a customer will churn or not. That program is called a predictive model.
You can use this model to predict business outcomes in any situation where you have input and historical output data:
Identify the business question you would like to ask.
Identify the historical input.
Identify the historically observed output (i.e., data samples for when the condition is true and for when it’s false).
For instance, if you want to predict who will pay the bills late, identify the input (customer demographics, bills) and the output (pay late or not), and let the machine learning use this data to create your model.
As you can see, machine learning can turn your business data into a financial asset. You can point the algorithm at your data so it can learn powerful rules that can be used to predict future outcomes. It’s no wonder predictive analytics is now the number one capability on product roadmaps, as demonstrated in Logi’s 2018 State of Embedded Analytics Report.
link:https://www.logianalytics.com/predictive-analytics/machine-learning-vs-traditional-programming
Basic Types Of Machine Learning
Machine Learning is the go-to toolbox of the current business operations in a variety of domains. The implementation of machine learning into such operations is a strategic step and requires a lot of resources. Therefore, it is essential to understand what kind of business task you want to your Machine Learning algorithm to work upon. You must also understand the different types of perks and flavors they bring to the table.
Based on the different flavors and objectives that a business can have, these machine learning algorithms are broadly classified as:
- Supervised Learning — “Teach me what to learn”
- Unsupervised Learning — “I will find what to learn”
- Reinforcement Learning — “I’ll learn from my mistakes at every step (Hit & Trial!)”
Example 1:
Reinforcement Learning:
Check the video below:
conclusion
Machine learning is a very powerful tool that allows you to perform multiple actions such as classifying data, teaching a program from experiments, or even creating an evolutionary program that is constantly improving. So even with a small sample (machine learning usually requires samples with 50 specimens) and data influenced by the subjectivity of the person measuring them, machine learning remains relatively accurate despite some shortcomings.
However, machine learning does not only have qualities, it must be constantly adapted to the problem it is trying to solve. Indeed, the programmer must first obtain the most representative sample possible. Then he will have to choose the function most faithful to the sample, which is not necessary in our case because the classes are sufficiently distinct so that changing the form of the function does not influence the results obtained. Finally, machine learning should be used as a tool because not all problems require a complex machine learning program. In our case, a simple basic determination key coupled with the experience of a botanist is much more effective.
Opening horizons
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