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Category: Les IA en un coup d’oeil

A.I. Helps Detect Breast Cancer That Doctors Might Overlook

A.I. Helps Detect Breast Cancer That Doctors Might Overlook

Breast cancer is one of the most life-threatening diseases that affects mostly women of all ages, ethnicities and social backgrounds. This is why medical professionals strive to detect these types of tumours in advance in order to treat them efficiently and save the patient’s life in the early stages of the disease. However, a major concern falls into place when we look at figures showing that more than 680,000 deaths were caused by breast cancer according to the WHO (World Health Organization) in 2020. Even with advancements in detection and diagnosis, some cases still get overlooked, resulting in late treatment and worse results.

Now, advancements in Artificial Intelligence are making it possible for A.I. to help doctors detect signs of tumours that the professionals may miss. So far, this tool is showing an impressive ability to spot cancer at least as well as human radiologists. In Hungary, where artificial intelligence is being tested in different hospitals, 22 cases of cancer have been detected by A.I. when these had gone unnoticed by radiologists.

These hospitals perform 35,000 screenings a year, which is a lot and, according to The National Cancer Institute, about 20 percent of breast cancers are missed during screening mammograms. Therefore, A.I. cut down on radiologists’ workloads by at least 30%, reducing the number of X-rays they needed to read.

This A.I. software for breast cancer detection will definitely improve public health.

However, even if this technology is showing serious advancements, it still needs to improve so it can be widely adopted. Firstly, additional clinical trials are needed. A.I. must show accurate results for women of all ages, ethnicities and body types, it has to cut down false positives that are not cancerous and most importantly, recognize more complex forms of breast cancer.

Also, people are still kind of sceptical about this new technology saying that it may replace human radiologists. Nevertheless, there’s nothing to be afraid of because patients will only trust this technology if it is used in cooperation with trained professionals. So A.I. will definitely not replace doctors, each mammogram is reviewed by 2 radiologists first, and then the A.I. agrees or flags areas that they need to check again.

In addition, more countries are willing to use the same technology in hospitals. The United States, Great Britain and the European Union, for example, are testing and providing data to develop the systems to detect breast cancers in their early stage.

In short, Artificial Intelligence will help detect signs of breast cancer that radiologists may miss. If it is used in partnership with trained doctors it will revolutionise detection and diagnosis of this disease. However, it has to improve its accuracy, show precision over diverse body types and ethnicities, limit false positives and, of course, detect complex shapes of breast cancer so it can be widely adopted. 

Using AI to interpret animal language

Using AI to interpret animal language

Can AI help human to get closer to nature ?

A scientific collaborative project on animal communication is under development and will potentially revolutionize bioacoustic domain.

This project named Earth Species Project whose goal is principally to be able to decode the language of animals has been launched in 2017. It is a non-profit organization headed by Katie Zacarian, Aza Raskin and Britt Selvitelle. Unfortunately, the human being having limits, it would be impossible for him alone to realize that task but artificial intelligence can.

To achieve this goal, the use of artificial intelligence is very helpful because of the automatic learning from this model that could be able to allow us to understand each animal’s signals and even them undetectable before by the human ear due to the too low frequencies.

A set of working tools is necessary for the development of this bioacoustic model such as voice recognition with the help of the encodeur AVES (Animal Vocalization Encoder based of Self Supervision) used with a database of audio recordings of animals sounds to be able to identify and to classify each sounds as well as a collect of data on the movements, of behavior and of the physiology of species to understand the signification of their sounds, or their language.

To give an exemple of the accuracy of their model, as part of their researches, they studied the voice signaling of beluga whales, a threatened species and as they give a cry to stay grouped, they realized measurements of these calls which allowed them to understand relationships between whales. Thanks to these tests, they were able to notice that the final analysis corresponded to the same did by experts in the domain.

A last important problem is to know if it exists a possible translation between animal and human language. Scientists has noticed that the human being language can be represented geometrically by a galaxy in multidimensional space where each point of that galaxy represents a word and its position in the space its link between other words. By that principe we can additionally notice that each galaxy corresponding to a human language has the same form. For a translation be possible, we should then have the two same forms.

Sources and additional informations:

https://en.wikipedia.org/wiki/Bioacoustics

https://www.youtube.com/watch?v=-4OFjxQQSaI – french short podcast that introduce the topic

https://www.actuia.com/actualite/comment-earth-species-project-utilise-lia-pour-decoder-la-communication-animale/ – french article

https://www.youtube.com/watch?v=oQFPL9JLQEY – documentary with explanations

https://www.youtube.com/watch?v=H9SvPs1cCds – longer video on the project development made by them

https://www.earthspecies.org/faq – their website

On the applications of Alphafold AI

On the applications of Alphafold AI

Alphafold is an AI designed to predict protein folding, therefore it can make predictions based on probabilities, according to the deep learning the program has been through, about the form/structure of proteins. Before briefly explaining the problem with protein folding and the role of Alphafold, we will see what uses this AI can have.

To make it simple, proteins go through four stages of folding : a primary structure of amino acids that forms a secondary helix/pleated sheet structure that itself folds (third stage), plus we have to take into account the possible folding occurring between multiple amino acids sequences, when a protein is made of multiple. The problem with all of this folding is that it requires a highly technical knowledge about thermodynamics and interatomic forces and possibly process thousands of folding points in a single protein. It is then extremely complicated for a human being to predict a protein structure. until 2018, the solution to predict those structures was to use expensive and long processes such as xray crystallography or nuclear magnetic resonance.

For that reason, Deepmind has been developing Alphafold, an AI that can predict protein structure when given the amino acid sequence. Alphafold has been trained on over 170,000 proteins whose structures are already identified. The newest version, Alphafold2 (2020) has a success rate of more than 90%. Roughly, Alphafold2 uses an attention mechanism rather than convolutions, which means some entry elements are given more computing power. With this mechanism, Alphafold computes step by step the relations between amino acids and takes isolated portions of the protein to compare them to similar already computed sequences. The prediction of Alphafold2 takes place in two steps : first makes graphs to compute the possible structure then it translates them in a 3d model.

As You can expect, Alphafold2 opens a new door to research. Indeed, easily and quickly computing protein structures allows researchers to study ANY protein. It can especially improve our understanding of proteins which need to have precise shapes to bind with the molecule they act on. To give a more appealing example, Alphafold2 was used to predict structures of proteins of SarsCov2 (the virus of Covid19) in early 2020, which leads researches on how the virus breaks out of host cells it replicates in. To give one more application of Alphafold2, researchers also use it in the genetic domain, as it can help them understand the genesis of proteins within the cell from mRNA, which means understanding with more detail how DNA regulates the internal machinery of a cell.

For nerds, here’s a good, albeit brain melting, video.

More sources :

https://en.wikipedia.org/wiki/AlphaFold

https://en.wikipedia.org/wiki/Protein_structure_prediction

This post was made by Johanny Titouan, 23/09/2023.

L’antiplagIA de GPT

L’antiplagIA de GPT

OpenIA la plus connue des IA génératives avec ChatGPT fournit aussi un outil permettant de détecter si un texte a été produit avec une IA : AI Text Classifier

Il faut saisir un minimum de 1000 caractères pour que la classification soit possible. La détection n’est pas sûre à 100%, surtout si le texte a été un peu retouché par un humain. Il arrive parfois que des textes produits par des enfants soient attribués à une IA …

https://platform.openai.com/ai-text-classifier

Andi

Andi

https://andisearch.com/

Andi est un chatbot de recherche précis et sans publicité. Il utilise un nouveau type de moteur de recherche alimenté par une IA générative et à une technologie de recherche sémantique. Au lieu de simples liens, Andi vous donne des réponses avec les sources.