ArTIFICIAL INTELLIGENCE
AI, machine learning, and deep learning are all popular terms frequently used when discussing all things data – they are the new, upcoming trends of technological change.
Together, with the rise of blockchain, these technologies are becoming the engines of the new technological paradigm that is radically changing our environment
Traditional statistical analysis has been performed by using standard tools such as regression.
Machine learning has expanded the power of these techniques and has enabled new capabilities such as image recognition and natural language processing. Deep learning ML models outperform legacy techniques by:
Increasing the predictive power of the existing models
Using advanced algorithms to solve new modeling tasks
Leveraging on knowledge acquired at MIT’s CSAIL, we develop cutting-edge AI solutions aimed at maximizing the performance of our clients. Some of our differentiating characteristics include:
- State-of-the-art deep learning neural network architectures
- Neural model augmentation via interpretability mechanisms
- Tailored user interfaces for streamlined model operations
- Model augmentation via interpretability mechanisms
Selected AI references
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AI
Artificial Intelligence
When we talk about the concept of Artificial Intelligence (AI) we are referring to one of the great advances that will mark the Digital Society in the coming years.
What is Artificial Intelligence? According to Wikipedia’s definition, AI is the processes by which “a machine imitates the ‘cognitive’ functions that humans associate with other human minds, such as learning and problem solving”.
The concept of Artificial Intelligence is not new, in fact we have to go back to 1956 to find the first reference to the term AI. It was developed by John McCarthy in 1956 based on the concepts of Alan Turing’s test, in which the parameters and ‘abilities’ that a machine should exhibit in order to be considered ‘intelligent’ were established. This concept, like the axioms contained in Moore’s Law, was more science fiction than reality in those years.
Since then, and especially in recent years, the development of Artificial Intelligence has been exponential and includes aspects from the first advances such as predicting behavior from a series of programmed guidelines (the first great example is the chess games with the Deep Blue supercomputer in the late 90s) to the development of industrial and manufacturing processes, the ability of machines for natural language processing (NLP), automated responses (chatbots), care and detection of diseases, education, robotics …
This development and new applications of Artificial Intelligence has generated a revision of its own definition and, according to Stuart Russel and Peter Norvig, we can now establish different categories of AI and distinguish between them:
They try to imitate human thinking processes, for example artificial neural networks (which we will deal with in this post) and include aspects such as decision making, problem solving or automatic learning.
These computer systems imitate human behavior; for example, robotics. Do you know the laws of robotics that the European Union is finalizing?
We are talking about a much deeper learning process, since these systems try to imitate or emulate the rational logical thinking of the human being.
A much further step in the evolution of Artificial Intelligence as we are talking about machines that try to rationally emulate human behavior and are capable of making decisions based on that reasoning.
And all this from the analysis, processing and interpretation of millions of data that, thanks to the technology of the Big Data become a fundamental base for the development and applications of the Artificial Intelligence in our day to day.
Machine Learning
Real-world impact
But What is Machine Learning? The definition of Machine Learning which is closely related to the advances and new uses of AI and which will be key in the development of digital transformation processes in companies, although also from an educational point of view.
When we talk about the definition of Machine Learning we are referring to one of the branches of Artificial Intelligence and its evolution towards autonomous learning systems by machines. Specifically, we are talking about machine learning, according to the Wikipedia definition which specifies that “it is about creating programs capable of generalizing behavior from information provided in the form of examples.
The evolution of Artificial Intelligence towards the concept of Machine Learning can be represented as the transition from the challenge of Deep Blue to Google’s Deep mind Alpha Go, which is capable of imagining worlds and projecting them from the analysis of millions of data and variables of human behavior in a matter of seconds.
What are Machine Learning’s applications? This capacity to learn and anticipate behavior has multiple uses, ranging from generalist uses such as facial recognition systems or the capacity for response and language learning, to more specialized uses such as the possibility of generating medical diagnoses or the systems that will serve as the basis for autonomous vehicles.
DEEP LEARNING
New step in the processes
From Artificial Intelligence to Machine Learning and from there to Deep Learning. A new step in the processes and evolution of Artificial Intelligence systems that is increasingly approaching the challenge of achieving computer systems that, autonomously and with hardly any human intervention, are capable of imitating human behavior and reasoning.
What is Deep Learning? With this concept we are referring to the set of algorithms that, like the neuronal networks of the human brain, are capable of generating responses and acting according to the conclusions reached by their combination, juxtaposition or contradiction from a system of layers that are ordered according to a hierarchy.
We are talking about a new level in the development of Artificial Intelligence and Machine Learning systems and, for the time being, the closest thing we can find to human thought. In fact, these neural networks can generate processes that include abstract thinking (own language and reasoning) or even, according to some experts, in the not too distant future, the ability to create.
AI
Artificial Intelligence
When we talk about the concept of Artificial Intelligence (AI) we are referring to one of the great advances that will mark the Digital Society in the coming years.
What is Artificial Intelligence? According to Wikipedia’s definition, AI is the processes by which “a machine imitates the ‘cognitive’ functions that humans associate with other human minds, such as learning and problem solving”.
The concept of Artificial Intelligence is not new, in fact we have to go back to 1956 to find the first reference to the term AI. It was developed by John McCarthy in 1956 based on the concepts of Alan Turing’s test, in which the parameters and ‘abilities’ that a machine should exhibit in order to be considered ‘intelligent’ were established. This concept, like the axioms contained in Moore’s Law, was more science fiction than reality in those years.
Since then, and especially in recent years, the development of Artificial Intelligence has been exponential and includes aspects from the first advances such as predicting behavior from a series of programmed guidelines (the first great example is the chess games with the Deep Blue supercomputer in the late 90s) to the development of industrial and manufacturing processes, the ability of machines for natural language processing (NLP), automated responses (chatbots), care and detection of diseases, education, robotics …
This development and new applications of Artificial Intelligence has generated a revision of its own definition and, according to Stuart Russel and Peter Norvig, we can now establish different categories of AI and distinguish between them:
They try to imitate human thinking processes, for example artificial neural networks (which we will deal with in this post) and include aspects such as decision making, problem solving or automatic learning.
These computer systems imitate human behavior; for example, robotics. Do you know the laws of robotics that the European Union is finalizing?
We are talking about a much deeper learning process, since these systems try to imitate or emulate the rational logical thinking of the human being.
A much further step in the evolution of Artificial Intelligence as we are talking about machines that try to rationally emulate human behavior and are capable of making decisions based on that reasoning.
And all this from the analysis, processing and interpretation of millions of data that, thanks to the technology of the Big Data become a fundamental base for the development and applications of the Artificial Intelligence in our day to day.
Machine Learning
Real-world impact
But What is Machine Learning? The definition of Machine Learning which is closely related to the advances and new uses of AI and which will be key in the development of digital transformation processes in companies, although also from an educational point of view.
When we talk about the definition of Machine Learning we are referring to one of the branches of Artificial Intelligence and its evolution towards autonomous learning systems by machines. Specifically, we are talking about machine learning, according to the Wikipedia definition which specifies that “it is about creating programs capable of generalizing behavior from information provided in the form of examples.
The evolution of Artificial Intelligence towards the concept of Machine Learning can be represented as the transition from the challenge of Deep Blue to Google’s Deep mind Alpha Go, which is capable of imagining worlds and projecting them from the analysis of millions of data and variables of human behavior in a matter of seconds.
What are Machine Learning’s applications? This capacity to learn and anticipate behavior has multiple uses, ranging from generalist uses such as facial recognition systems or the capacity for response and language learning, to more specialized uses such as the possibility of generating medical diagnoses or the systems that will serve as the basis for autonomous vehicles.
DEEP LEARNING
New step in the processes
From Artificial Intelligence to Machine Learning and from there to Deep Learning. A new step in the processes and evolution of Artificial Intelligence systems that is increasingly approaching the challenge of achieving computer systems that, autonomously and with hardly any human intervention, are capable of imitating human behavior and reasoning.
What is Deep Learning? With this concept we are referring to the set of algorithms that, like the neuronal networks of the human brain, are capable of generating responses and acting according to the conclusions reached by their combination, juxtaposition or contradiction from a system of layers that are ordered according to a hierarchy.
We are talking about a new level in the development of Artificial Intelligence and Machine Learning systems and, for the time being, the closest thing we can find to human thought. In fact, these neural networks can generate processes that include abstract thinking (own language and reasoning) or even, according to some experts, in the not too distant future, the ability to create.