Pablo Picasso once declared: "Computers are useless. They can only give you answers." The Spanish artist's joke may have been true of the 20th century, when computers were for the most part souped-up calculating machines performing clearly prescribed functions. But the expansion of computing power in the early 21st century means that computers are now posing some of the most testing questions of our times. And it is not clear who is responsible for providing the answers.

In the late '90s, Tomi Poutanen, a precocious computer whiz from Finland, hoped to do his dissertation on neural networks, a scientific method aimed at teaching computers to act and think like humans. As a student at the University of Toronto, it was a logical choice. Geoffrey Hinton, the godfather of neural network research, taught and ran a research lab there. But instead of encouraging Poutanen, who went on to work at Yahoo and recently co-founded media startup Milq, one of his professors sent a stern warning about taking the academic path known as deep learning. "Smart scientists," his professor cautioned, "go there to see their careers end."

In an age when technology companies routinely introduce new forms of everyday magic, one problem that remains seemingly unsolved is that of long-form transcription. Sure, voice dictation for documents has been conquered by Nuance's Dragon software. Our phones and smart home devices can understand fairly complex commands, thanks to self-teaching recurrent neural nets and other 21st century wonders. However, the task of providing accurate transcriptions of long blocks of actual human conversation remains beyond the abilities of even today's most advanced software. When solved on a broad scale, it is a problem that might unlock vast archives of oral histories, make podcasts easier to consume for speed-readers (tl;dl), and be a world-changing boon for journalists everywhere, liberating precious hours of sweet life.

In what they called a milestone achievement for artificial intelligence, scientists said on Wednesday they have created a computer program that beat a professional human player at the complex board game called Go, which originated in ancient China. The feat recalled IBM supercomputer Deep Blue's 1997 match victory over chess world champion Garry Kasparov. But Go, a strategy board game most popular in places like China, South Korea and Japan, is vastly more complicated than chess. "Go is considered to be the pinnacle of game AI research," said artificial intelligence researcher Demis Hassabis of Google DeepMind, the British company that developed the AlphaGo program. "It's been the grand challenge, or holy grail if you like, of AI since Deep Blue beat Kasparov at chess."

We often get questions about whether to use Python or R – and we've come to a conclusion thanks to insight from our community of mentors and learners. Data science is the sexiest job of the 21st century. Data scientists around the world are presented with exciting problems to solve. Within the complex questions they have to ask, a growing mountain of data rests a set of insights that can change entire industries. In order to get there, data scientists often rely on programming languages and tools. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May.

Jon Levin, a professor of economics at Stanford, recently wrote in Forbes, "Machine learning methods are really powerful for fitting predictive models and for doing classification on large-scale, high-dimensional data. These are the data we increasingly use in economics. So I think there's no doubt many machine learning methods will get used more and more often. One area that's going to get a lot of attention is combining machine learning with causal inference. A big fraction of empirical microeconomics is about finding ways to exploit natural experiments, whether by using instrumental variables, regression discontinuity, matching, difference-in-difference estimators, or other methods."

Episode seven of season two is a little different than our usual episodes, Ryan and Katherine just returned from a conference where they got to talk with Neil Lawrence of the University of Sheffield about some of the larger issues surrounding machine learning and society. They discuss anthropomorphic intelligence, data ownership, and the ability to empathize. The entire episode is given over to this conversation in hopes that it will spur more discussion of these important issues as the field continues to grow.

Predictive Modeling works on constructive feedback principle. Get feedback from metrics, make improvements and continue until you achieve a desirable accuracy. Evaluation metrics explain the performance of a model. An important aspects of evaluation metrics is their capability to discriminate among model results. Once they are finished building a model, they hurriedly map predicted values on unseen data. This is an incorrect approach. Simply, building a predictive model is not your motive. But, creating and selecting a model which gives high accuracy on out of sample data.

Today I was invited to give a KeyNote Lecture about Artificial Intelligence in the beautiful city of Zaragoza, by Javier Khunel the CEO of the main business school there, media group Heraldo and CaixaBank, to a diverse audience of business owners, entrepreneurs, c level execs, intrapreneurs and many more, at a great venue The CaixaForum building. I have been in the field for the last 21 years, and the last ones as clear advocate of AI and Deep Learning, with a company in the field, and advising Emotiv Inc the Leader in Brain Computer Interfaces about Data Science and Artificial Intelligence. So it is fair to say that I play in a field I know very well. Anyway I always get my facts and figures up to date, and to my surprise I discover an amazing fact: the father of Artificial Intelligence according to the MIT Technology Review is from my own backyard so to speak, from the country I was born Spain. He develop a machine in 1912 called "El Ajedrecista" or "The Chess Player" a very limited precursor of IBM's Deep Blue, and the first true chess computer, but by all means a pioneer (electro mechanical) work in the Artificial Intelligence field, he also build an Algebraic Formula Machine and many other mostly unknown marvels.

The Swedish startup company eEducation Albert is now launching an artificial intelligence application with focus on helping pupils with their homework in math during the last years of primary school. The startup-company intend to expand to other education-levels as well as the international market during the coming years. At the moment AI Albert is filled all available answers to questions to make good work with the pupils and the next week a paid version of the application is being launched to 65 schools in Sweden. "We have created a digital person that we have feeded with advanced logic and knowledge on how you teach the textbook the pupil is sitting with", says one of the founders Arta Mandegari in an interview with di.se. He and the other founder Salman Eskandari is graduates from the Chalmers, technological university of Gothenburg.