Scientists have just announced their new discovery. It turns out that there is a second hidden code in human DNA that contains information that can change the way we read the instructions in our DNA and interpret mutations that affect health and disease.
Scientific communities expect the new discovery to help better diagnose the disease as well as develop new treatments. The discovery was made as part of the work of the ENCODE project, which aimed to thoroughly investigate what elements of DNA are essential for storing information about biological functions programmed into the human genome.
Ever since the DNA code was first decoded, it was thought to be responsible only for the encoding of information about proteins. Now, however, it has turned out that there is a second language sewn into the genome. One actually describes making proteins, and the other instructs cells to control their genes. One of the languages is written above the other and that is why no one has discovered it for so long.
This amazing discovery is further evidence that DNA is an extremely powerful "device" for storing information, which nature is very willing and skillful to use. Its similarity to modern computer programming languages is amazing.
The fact that the genetic code can record two types of information simultaneously also means that many changes to DNA at this level of data recording can also lead to disease by disrupting the genetic control of cells.
Recent research shows that as many as 40% of the population can be infected with toxoplasmosis, a type of brain parasite that can affect a person's behavior.
According to John Webster, professor of epidemiology at the Imperial College of London, brain parasites are isolated and protected from the immune system. However, you should be aware that they can affect people's behavior. Toxoplasmosis is common in domestic cats. According to the estimates of the aforementioned scientist, approximately 350,000 British people are infected with the parasite every year. Czech scientist Jaroslav Flegrei believes that toxoplasmosis also leads to depression and suicide.
The parasite occurs in the form of microscopic cysts. Its presence was found in two areas of the brain: the one responsible for fear and pleasure. Recent studies have shown that DNA toxoplasmosis includes two genes that increase dopamine secretion. When this was tested in mice, toxoplasmoses suddenly stopped responding to the smell of cat urine after being infected with the protozoan. Dopamine choked off natural instincts. Scientists suggest that some unsafe car drivers may be carriers of a parasite that affects their driving style, making it more chaotic and dangerous.
MIT researchers say they have developed a simple tool that helps analysts predict the future based on time-series data. Defined as a collection of observations recorded over a period of time, time series data and its forecasts are critical for resource analysis, medical diagnosis, and even weather forecasting.
Nobody knows what the future holds for us. Less scientific approaches to predicting the future involve humans such as media, media, and astrological predictors. However, such practices and their methods have not been able to consistently produce scientifically valid results. In contrast, mathematicians and statisticians dealing with data analysis often use predictive analytics tools to offer a fairly accurate picture of possible future events. Unfortunately, most of these forecasting systems rely on complex algorithms and significant computing power, making them largely inaccessible to ordinary time series analysts.
The MIT researchers say they changed this equation by developing a simplified future prediction algorithm that any researcher could use. Making predictions from time series data typically requires multiple steps in data processing and the use of complex machine learning algorithms that have a learning curve so long that they are not readily available to laymen. That's why experts come to the rescue with a new tool, tspDB (Time Series Forecasting Database). Unlike other complex data analysis and forecasting tools, tspDB "takes care of all the complex modeling behind the scenes so that the layman can easily create a forecast in just a few seconds."
Surprisingly, the team behind the new system says tspDB is more accurate and efficient than almost all current deep learning methods in two key areas: predicting future values and filling in missing data points. The program researcher Abdullah Alomar says the performance is due to tspDB's use of a "new time series prediction algorithm" that is extremely effective at analyzing multivariate time series data. An example is weather analysis where indicators such as cloud cover, temperature and dew point depend on past values.
In the published results, the MIT team explains how it tested the tspDB system against competing algorithms, including advanced deep learning techniques, by analyzing real-time series datasets. These included data from the electricity grid, traffic patterns, and financial markets. As expected, the new algorithm performed brilliantly, outperforming all other systems tested to predict future values except one. One of the reasons it works so well is because the model captures a lot of the dynamics of the time series, but ultimately it's still a simple model.
As it stands, the MIT prediction tool can be installed on an existing database, allowing researchers to fill out a prediction query "with just a few keystrokes in approximately 0.9 milliseconds, compared to 0.5 milliseconds for a standard search query". The researchers note that with unprecedented speed and accuracy, their predictive tool becomes even more accurate the more data is added to the system.
The MIT team praises the system's efficiency and accuracy, but says their efforts are driven by the system's ease of use for random researchers. In this context, it is important that tspDB succeeds as a widely used open source system. Time series data is very important and it's a great idea to build predictive functions right in your database.
Artificial intelligence is an extremely useful tool that is more and more willingly used in many different fields, and its continuous development means that it has more and more applications. In recent research, scientists have shown that AI can learn to recognize gaps in people's habits and behaviors, and then use them to influence human decisions.
A research team from the Australian research agency CSIRO (Commonwealth Scientific and Industrial Research Organization) has developed a method to detect and exploit weak points in the way people make choices. He then tested his model by running three experiments in which human volunteers played games against the computer.
In the first experiment, participants clicked on red or blue boxes to earn bogus money. Artificial intelligence learned the participants' selection patterns and led them to make a specific choice. SI showed an efficiency of 70%.
In a second experiment, volunteers were tasked with observing the screen and pressing a button when a specific symbol was displayed. Here, the artificial intelligence arranged the order of the symbols in such a way that the participants made as many mistakes as possible. It turned out that the participants of the study made 25% more mistakes.
The last experiment consisted of several rounds. The participants played the role of an investor and were to transfer money to the trustee, i.e. artificial intelligence. The AI then returned the participants a certain amount of money, and players had to decide how much money they would invest in the next round. In the first mode, artificial intelligence tried to obtain as much cash as possible, and in the second mode, it sought a fair distribution of money between itself and the investors, i.e. the participants of the game. It turned out that artificial intelligence was successful in every mode.
In each experiment, the machine learned from the participants' responses and identified and targeted weak points in the decision-making process. In this way, the AI learned to manipulate participants into taking certain actions.
The latest research not only allows you to create a system that can effectively mislead you. The results of the experiments also help us understand how people make choices. Artificial intelligence can be used for good or bad purposes - it can, for example, detect people's weaknesses and in certain situations help to avoid bad choices or exert a negative influence on public opinion.
No comments:
Post a Comment