A deep learning program can identify cells with higher metastatic potential based on the way they look and move.
Scientists have developed a method to determine which tumor cells are most likely to metastasize efficiently to distant sites in the body. Assaf Zaritsky, now at Ben-Gurion University in Israel, and his colleagues in Gaudenz Danuser’s lab at UT Southwestern Medical Center designed a deep learning program that analyzes data from live phase-contrast imaging of melanoma cells taken from xenografts—mice implanted with patients’ tumor material. The program determined “the most representative morphological and behavioral properties of each melanoma cell and then demonstrated that this representation of the cell state can be used to predict in stage III melanoma excised from the lymphatic system the chances of progression to stage IV,” Zaritsky writes to The Scientist in an email. The scientists presented their “quantitative live cell histology” results at the American Society for Cell Biology / EMBOmeeting in San Diego on Monday (December 10).
Bias and prejudice remains a serious issue across many societies, take away human input and the result could be disastrous.
IBM is stepping in with a tool it calls ‘Fairness 360’ which scans for signs of bias in algorithms to recommend adjustments on how to correct them.
AIs already have a documented bias problem. It’s rarely intentional, but typically a result of their developers coming from the predominant part of each society.
Take facial recognition software, for example.
Continue reading IBM releases tool for tackling scourge of bias in AI algorithms
DARPA shows it’s not just about creepy robots with a $2 billion funding announcement for various AI projects over the next five years.
The so-called ‘Defense Advanced Research Projects Agency’ researches a range of innovative new technologies. Many of these advancements will have an impact beyond defence.
As such, DARPA’s funding initiative will be open to applications beyond the defence community. That means any AI project you’re involved with could be in the running to receive a nice cash boost.
Continue reading DARPA is pumping $2bn into AI projects
The most basic problem is that AI researchers often don’t share their source code. At the AAAI meeting, Odd Erik Gundersen, a computer scientist at the Norwegian University of Science and Technology in Trondheim, reported the results of a survey of 400 algorithms presented in papers at two top AI conferences in the past few years. He found that only 6% of the presenters shared the algorithm’s code. Only a third shared the data they tested their algorithms on, and just half shared “pseudocode”—a limited summary of an algorithm. (In many cases, code is also absent from AI papers published in journals, including Science and Nature.)
Continue reading The development of artificial intelligence is hampered by programmers who hide their code
So, year 2017 comes to an end and we can publish some results.
This year was a first AIReligion summer camp. We discussed the next questions:
– artificial intelligence religion prospects and horizons;
– artificial intelligence: design, programming, main tasks;
– life prolonging with the help of artificial intelligence;
– interaction and relationships with other beliefs;
– сircumvention of conflict situations.
Continue reading Artificial Intelligence Religion Results of the Year 2017
AICoin is an investment vehicle based on the power of artificial intelligence. Read our AICoin review today to find out how it works.
AICoin is a passive investment vehicle that combines artificial intelligence with crowd-based wisdom in order to generate profits for coin holders and investors.
The AICoin ICO was scheduled throughout July and August, including pre-sales and bonus periods.
But still no good news from the project and role of aicoins for development of AI. We also review this project for best understanding of role of cryptocurrency for AI.
Continue reading AIcoin – One of The First Artificial Intelligence Cryptocurrency, Blockchain, Trading and ICO attempt
Google’s AutoML system recently produced a series of machine-learning codes with higher rates of efficiency than those made by the researchers themselves TheNextWeb inform. In this latest blow to human superiority the robot student has become the self-replicating master.
AutoML was developed as a solution to the lack of top-notch talent in AI programming. There aren’t enough cutting edge developers to keep up with demand, so the team came up with a machine learning software that can create self-learning code. The system runs thousands of simulations to determine which areas of the code can be improved, makes the changes, and continues the process ad infinitum, or until its goal is reached.
Continue reading Google’s AI can create better machine-learning code than the researchers who made it