Tag Archive for: neural networks

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Comprendre informations du langage, algorithms

Making algorithms understand what we are talking about

20 April 2021/0 Comments/in Digital, In the News/by I'MTech

Human language contains different types of information. We understand it all unconsciously, but explaining it systematically is much more difficult. The same is true for machines. The NoRDF Project Chair “Modeling and Extracting Complex Information from Natural Language Text” seeks to solve this problem: how can we teach algorithms to model and extract complex information from language? Fabian Suchaneck and Chloé Clavel, both researchers at Telecom Paris, explain the approaches of this new project

What aspects of language are involved in making machines understand?

Fabian Suchaneck: We need to make them understand more complicated natural language texts. Current systems can understand simple statements. For example, the sentence: “A vaccine against Covid-19 has been developed” is simple enough to be understood by algorithms. On the other hand, they cannot understand sentences that go beyond a single statement, such as: “If the vaccine is distributed, the Covid-19 epidemic will end in 2021. In this case, the machine does not understand that the condition required for the Covid-19 epidemic to end in 2021 is that the vaccine is distributed. We also need to make machines understand what emotions and feelings are associated with language; this is Chloé Clavel’s specialist area.

What are the preferred approaches in making algorithms understand natural language?

FS: We are developing “neurosymbolic” approaches, which seek to combine symbolic approaches with deep learning approaches. Symbolic approaches use human-implemented logical rules that simulate human reasoning. For the type of data we process, it is fundamental to be able to interpret what has been understood by the machine afterwards. Deep learning is a type of automatic learning where the machine is able to learn by itself. This allows for greater flexibility in handling variable data and the ability to integrate more layers of reasoning.

Where does the data you analyze come from?

FS: We can collect data when humans interact with chatbots from a company and especially those from the project’s partner companies. We can extract data from comments on web pages, forums and social networks.

Chloé Clavel: We can also extract information about feelings, emotions, social attitudes, especially in dialogues between humans or humans with machines.

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What are the main difficulties for the machine in learning to process language?

CC: We have to create models that are robust in changing contexts and situations. For example, there may be language variability in the expression of feelings from one individual to another, meaning that the same feelings may be expressed in very different words depending on the person. There is also a variability of contexts to be taken into account. For example, when humans interact with a virtual agent, they will not behave in the same way as with a human, so it is difficult to compare data from these different sources of interactions. Yet, if we want to move towards more fluid and natural human-agent interactions, we must draw inspiration from the interactions between humans.

How do you know whether the machine is correctly analyzing the emotions associated with a statement?

CC: The majority of the methods we use are supervised. The data entered into the models are annotated in the most objective way possible by humans. The goal is to ask several annotators to annotate the emotion they perceive in a text, as the perception of an emotion can be very subjective. The model is then taught about the data for which a consensus among the annotators could be found. When testing the performance of the model, when we inject an annotated text into a model that has been trained with similar texts, we can see if the annotation it produces is close to those determined by humans.

Since the annotation of emotions is particularly subjective, it is important to determine how the model actually understood the emotions and feelings present in the text. There are many biases in the representativeness of the data that can interfere with the model and mislead us on the interpretation made by the machine. For example, if we assume that younger people are angrier than older people in our data and that these two categories do not express themselves in the same way, then it is possible that the model may end up simply detecting the age of the individuals and not the anger associated with the comments.

Is it possible that the algorithms end up adapting their speech according to perceived emotions?

CC: Research is being conducted on this aspect. Chatbots’ algorithms must be relevant in solving the problems they are asked to solve, but they must also be able to provide a socially relevant response (e.g. to the user’s frustration or dissatisfaction). These developments will improve a range of applications, from customer relations to educational or support robots.

What contemporary social issues are associated with the understanding of human language by machines?

FS: This would notably allow a better understanding of the perception of news on social media by humans, the functioning of fake news, and therefore in general which social group is sensitive to which type of discourse and why. The underlying reasons why different individuals adhere to different types of discourse are still poorly understood today. In addition to the emotional aspect, there are different ways of thinking that are built in argumentative bubbles that do not communicate with each other.

In order to be able to automate the understanding of human language and exploit the numerous data associated with it, it is therefore important to take as many dimensions into account as possible, such as the purely logical aspect of what is said in sentences and the analysis of the emotions and feelings that accompany them.

By Antonin Counillon

Shedding some light on black box algorithms

7 April 2021/0 Comments/in Digital, In the News/by I'MTech

In recent decades, algorithms have become increasingly complex, particularly through the introduction of deep learning architectures. This has gone hand in hand with increasing difficulty in explaining their internal functioning, which has become an important issue, both legally and socially. Winston Maxwell, legal researcher, and Florence d’Alché-Buc, researcher in machine learning, who both work for Télécom Paris, describe the current challenges involved in the explainability of algorithms.

What skills are required to tackle the problem of algorithm explainability?

Winston Maxwell: In order to know how to explain algorithms, we must draw on different disciplines. Our multi-disciplinary team, AI Operational Ethics, focuses not only on mathematical, statistical and computational aspects, but also on sociological, economic and legal aspects. For example, we are working on an explainability system for image recognition algorithms used, among other things, for facial recognition in airports. Our work therefore encompasses these different disciplines.

Why are algorithms often difficult to understand?

Florence d’Alché-Buc: Initially, artificial intelligence used mainly symbolic approaches, i.e., it simulated the logic of human reasoning. Logical rules, called expert systems, allowed artificial intelligence to make a decision by exploiting observed facts. This symbolic framework made AI more easily explainable. Since the early 1990s, AI has increasingly relied on statistical learning, such as decision trees or neural networks, as these structures allow for better performance, learning flexibility and robustness.

This type of learning is based on statistical regularities and it is the machine that establishes the rules which allow their exploitation. The human provides input functions and an expected output, and the rest is determined by the machine. A neural network is a composition of functions. Even if we can understand the functions that compose it, their accumulation quickly becomes complex. So a black box is then created, in which it is difficult to know what the machine is calculating.

How can artificial intelligence be made more explainable?

FAB: Current research focuses on two main approaches. There is explainability by design where, for any new constitution of an algorithm, explanatory output functions are implemented which make it possible to progressively describe the steps carried out by the neural network. However, this is costly and impacts the performance of the algorithm, which is why it is not yet very widespread. In general, and this is the other approach, when an existing algorithm needs to be explained, it is an a posteriori approach that is taken, i.e., after an AI has established its calculation functions, we will try to dissect the different stages of its reasoning. For this there are several methods, which generally seek to break the entire complex model down into a set of local models that are less complicated to deal with individually.

Why do algorithms need to be explained?

WM: There are two main reasons why the law stipulates that there is a need for the explainability of algorithms. Firstly, individuals have the right to understand and to challenge an algorithmic decision. Secondly, it must be guaranteed that a supervisory institution such as the French Data Protection Authority (CNIL), or a court, can understand the operation of the algorithm, both as a whole and in a particular case, for example to make sure that there is no racial discrimination. There is therefore an individual aspect and an institutional aspect.

Does the format of the explanations need to be adapted to each case?

WM: The formats depend on the entity to which it needs to be explained: for example, some formats will be adapted to regulators such as the CNIL, others to experts and yet others to citizens. In 2015, an experimental service to deploy algorithms that detect possible terrorist activities in case of serious threats was introduced. For this to be properly regulated, an external control of the results must be easy to carry out, and therefore the algorithm must be sufficiently transparent and explainable.

Are there any particular difficulties in providing appropriate explanations?

WM: There are several things to bear in mind. For example, information fatigue: when the same explanation is provided systematically, humans will tend to ignore it. It is therefore important to use varying formats when presenting information. Studies have also shown that humans tend to follow a decision given by an algorithm without questioning it. This can be explained in particular by the fact that humans will consider from the outset that the algorithm is statistically wrong less often than themselves. This is what we call automation bias. This is why we want to provide explanations that allow the human agent to understand and take into consideration the context and the limits of algorithms. It is a real challenge to use algorithms to make humans more informed in their decisions, and not the other way around. Algorithms should be a decision aid, not a substitute for human beings.

What are the obstacles associated with the explainability of AI?

FAB: One aspect to be considered when we want to explain an algorithm is cyber security. We must be wary of the potential exploitation of explanations by hackers. There is therefore a triple balance to be found in the development of algorithms: performance, explainability and security.

Is this also an issue of industrial property protection?

WM: Yes, there is also the aspect of protecting business secrets: some developers may be reluctant to discuss their algorithms for fear of being copied. Another counterpart to this is the manipulation of scores: if individuals understand how a ranking algorithm, such as Google’s, works, then it would be possible for them to manipulate their position in the ranking. Manipulation is an important issue not only for search engines, but also for fraud or cyber-attack detection algorithms.

How do you think AI should evolve?

FAB: There are many issues associated with AI. In the coming decades, we will have to move away from the single objective of algorithm performance to multiple additional objectives such as explainability, but also equitability and reliability. All of these objectives will redefine machine learning. Algorithms have spread rapidly and have enormous effects on the evolution of society, but they are very rarely accompanied by instructions for their use. A set of adapted explanations must go hand in hand with their implementation in order to be able to control their place in society.