Please introduce yourself and your Weps to our readers! We are a team of 20-somethings from Estonia who are behind Weps, the artificial intelligence chatbot website builder that works on any device. Our users can create websites by interacting with Weps in an already familiar messenger-like interface. We were just accepted into Axel Springer Plug and Play accelerator this September and are looking forward to grow together with their help! How did you get the idea to Weps?

When it comes to the future of diagnosing and treating cancer, computers – not humans – could hold the key to delivering the best quality precision medicine. A new study out of the Stanford University School of Medicine has found that computers can be trained to more accurately assess slides of lung cancer tissue than pathologists. "Pathology as it is practiced now is very subjective," said Michael Snyder, PhD, professor and chair of genetics. "Two highly skilled pathologists assessing the same slide will agree only about 60 percent of the time. This approach replaces the subjectivity with sophisticated, quantitative measurements that we feel are likely to improve patient outcomes."

My learning preference is to watch lecture videos and thankfully there are several excellent courses online.

Basic machine learning concepts of Bias vs Variance Tradeoff, Avoiding overfitting, Bayesian inference and Occam razor, Feature combination, Non-linear basis functions, and more - explained via pictures.

Autodesk is one of the most proactive companies when it comes bringing new ideas into the PLM and CAD software arena. This is especially true when it comes to solutions hosted in the cloud. Last week the company added another piece of software to its portfolio. Design Graph uses new technologies to tackle the age old question of design data management. It all began with the developers at Autodesk asking themselves the question: How can we better organize design information in a way that is more in line with how humans think and create?

Data Envelopment Analysis, also known as DEA, is a non-parametric method for performing frontier analysis. It uses linear programming to estimate the efficiency of multiple decision-making units and it is commonly used in production, management and economics. The technique was first proposed by Charnes, Cooper and Rhodes in 1978 and since then it became a valuable tool for estimating production frontiers. Update: The Datumbox Machine Learning Framework is now open-source and free to download. When I first encountered the method 5-6 years ago, I was amazed by the originality of the algorithm, its simplicity and the cleverness of the ideas that it used. I was even more amazed to see that the technique worked well outside of its usual applications (financial, operation research etc) since it could be successfully applied in Online Marketing, Search Engine Ranking and for creating composite metrics.

The Best Paper award for this year's International Conference on Very Large Data Bases (VLDB) goes to "Compressed Linear Algebra for Large-Scale Machine Learning", authored by a PhD candidate at the University of Maryland and four senior researchers from IBM. Their method for compressing matrices for linear algebra operations promises to provide users significant increases in speed with less memory. In particular, the compression technology provides benefits at two different parts of the data science process. Before training a model, a data scientist typically goes through multiple iterations of feature engineering. Common feature engineering tasks include examining the data with descriptive statistics and transforming the values in columns to better suit the assumptions built into different types of machine learning models.

When we see two people meet, we can often predict what happens next: A handshake, a hug, or maybe even a kiss. Our ability to anticipate actions is thanks to intuitions born out of a lifetime of experiences. Machines, on the other hand, have trouble making use of complex knowledge like that. Computer systems that predict actions would open up new possibilities ranging from robots that can better navigate human environments, to emergency response systems that predict falls, to Google Glass-style headsets that feed you suggestions for what to do in different situations. This week researchers from MIT's Computer Science and Artificial Intelligence Laboratory(CSAIL) have made an important new breakthrough in predictive vision, developing an algorithm that can anticipate interactions more accurately than ever before.

There is a surge of interest and research into Artificial Intelligence (AI). AI is seen as the new technological revolution in the work place with machines tipped to replace most human jobs. There is significant improvement in the field of AI currently, than in the history of mankind, AI has been branded a failure or a hype in the past, but that notion does not seem to be the case anymore with the emergence of more sophisticated computer algorithms that have helped machines to pass the Turing Test – A test which determines whether or not a machine computer is capable of thinking like a human. Critics have branded the Turing test an emotionally unsatisfying test for intelligence but agreed that a machine passing the Turin test is an important milestone for AI. It is important to highlight that to get more conclusive results from tests of AI, the Turin test has had twists and variations from the original test by Alan Turing.