Dr. Richard Sutton presents "The Future of Artificial Intelligence" in the Technology and Future of Medicine course LABMP 590 http://www.singularitycourse.com This video has the greatest potential to save the world, and improve everyone's preparation for the future and improve the actual future itself, of any videos we have produced to date. If you do not want to watch the whole thing from the beginning, watch from 00:27:24 The Enslavement Problem. This is from the September 10th, 2015 lecture in eHub at the University of Alberta in Edmonton, Canada. Dr. Kim Solez gave a poetry reading on subjects related to this lecture at the Strathearn Art Walk on September 12, 2015, with Dr. Sutton in the audience and commenting afterward see https://www.youtube.com/watch?v jTVO7... .

But perhaps the real action in artificial intelligence is happening at the Hilton San Francisco, where a much less flashy tech crowd gathered this week. There, hundreds of engineers (2,500 to be precise), geeked out over fireside chats on topics such as "streaming data in real time" and "building accelerators in databases." The event, called the Spark Summit, brings together the growing number of developers who are working with Spark, perhaps the hottest data-mining software today. One way to understand future of artificial intelligence and how it will transform the economy is to watch what everyone is talking about at the Spark Summit. Engineers from companies such as Capital One to Airbnb spoke about their successful big data projects in pure engineering-ese.

It was an historic week as Hillary Clinton secured enough delegates to be the first woman to become the presumptive nominee of a major political party for the highest office of the most powerful country on Earth. Meanwhile, we're reviewing another debate about where Seattle's startup ecosystem ranks nationally; new data on urban startup clusters; a 15 million funding round for BitTitan; Bill Gates' poultry program; news of artificial intelligence watching and writing sci-fi films; and a good read on how "Silicon Valley" delivers such an accurate satire of the real Silicon Valley. It doesn't rank as high as NYC, LA, or Boston in the number of startups funded or capital invested. So on a dollars in/dollars [out], Seattle outperforms. The perennial debate about who's No. 2 (always behind Silicon Valley) is tiresome, but look, here I am writing about it. One really good thing to come out of this is Tren Griffin's essay at GeekWire on what it takes for a city to develop a top-tier tech and startup ecosystem.

Learning Granger causality for general point processes is a very challenging task. In this paper, we propose an effective method, learning Granger causality, for a special but significant type of point processes --- Hawkes process. We reveal the relationship between Hawkes process's impact function and its Granger causality graph. Specifically, our model represents impact functions using a series of basis functions and recovers the Granger causality graph via group sparsity of the impact functions' coefficients. We propose an effective learning algorithm combining a maximum likelihood estimator (MLE) with a sparse-group-lasso (SGL) regularizer. Additionally, the flexibility of our model allows to incorporate the clustering structure event types into learning framework. We analyze our learning algorithm and propose an adaptive procedure to select basis functions. Experiments on both synthetic and real-world data show that our method can learn the Granger causality graph and the triggering patterns of the Hawkes processes simultaneously.

A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sample. Such predictions are valuable for developing hypotheses for selecting therapies tailored for individual patients. This is especially valuable in oncology, where molecular and genetic heterogeneity of the cells has a major impact on the response. However, the prediction task is extremely challenging, raising the need for methods that can effectively model and predict drug responses. In this study, we propose a novel formulation of multi-task matrix factorization that allows selective data integration for predicting drug responses. To solve the modeling task, we extend the state-of-the-art kernelized Bayesian matrix factorization (KBMF) method with component-wise multiple kernel learning. In addition, our approach exploits the known pathway information in a novel and biologically meaningful fashion to learn the drug response associations. Our method quantitatively outperforms the state of the art on predicting drug responses in two publicly available cancer data sets as well as on a synthetic data set. In addition, we validated our model predictions with lab experiments using an in-house cancer cell line panel. We finally show the practical applicability of the proposed method by utilizing prior knowledge to infer pathway-drug response associations, opening up the opportunity for elucidating drug action mechanisms. We demonstrate that pathway-response associations can be learned by the proposed model for the well known EGFR and MEK inhibitors.

Machine learning algorithms and artificial intelligence tools are receiving a lot of attention in the analytics world these days, and industry experts and experienced users say the plaudits are well deserved. "These models are making a big difference, and if you're not considering how to use them in your product, you probably should," said Jeff Dean, a senior fellow at Google who helped lead development of TensorFlow, the company's open source machine learning platform. Machine learning has come to play a central role in the majority of new products Google develops, Dean said in a presentation at Spark Summit 2016 in San Francisco. For example, it's at the core of training speech recognition tools used in the Android mobile operating system. Machine learning technology also helped Google create a tool that automatically tags photos uploaded by users by examining what's happening in the photo.

The Internet of Things (IoT), Artificial Intelligence (AI) and cloud computing are three technologies that are converging to disrupt nearly every industry. IoT refers to a connected network of objects embedded with technology that enables the collection and exchange of data. Cloud computing is the storing and retrieval of data, and accessing application programs via the Internet. Artificial Intelligence is the simulation of human intelligence by machines. We are currently in the midst of the rise of the first wave of this technological convergence.

In the wake of Google's AI Go victory, filmmaker Oscar Sharp turned to his technologist collaborator Ross Goodwin to build a machine that could write screenplays. They created "Jetson" and fueled him with hundreds of sci-fi TV and movie scripts. Building a team including Thomas Middleditch, star of HBO's Silicon Valley, they gave themselves 48 hours to shoot and edit whatever Jetson decided to write. Lyrics by Benjamin (formerly known as Jetson), an LSTM RNN Artificial Intelligence.

A University of Surrey professor was'overwhelmed' after being appointed a CBE in the Queen's Birthday honours for his career and work in sociology and engineering. Professor Nigel Gilbert founded the Social and Computer Sciences research group in 1984 which focuses on applying social science to the design of artificial intelligence systems. He established the Centre for Research in Social Simulation in 1997 and it is still based at the Guildford university campus. Prof Gilbert said of being appointed a Commander of the Order of the British Empire: "It was very overwhelming. I received the letter about three weeks ago, a brown envelope from the cabinet office. "At first I thought it was a tax bill.

Quoting unnamed sources, the news agency says Amazon's music service will be priced like competitors, at 9.99 monthly, and looks to launch in late summer. Amazon currently has Prime Music, a unit of the 99 yearly Prime entertainment offering, but its music selections are way slimmer than rivals. What Amazon has that competitors don't is a hugely popular home speaker, Echo, which is used to control the smart home, answer queries, order products and play online music from Amazon's small selection, and via Spotify. Google is launching a similar speaker product later in the year, Google Home. Spotify is the market leader in subscription music, with 30 million subscribers, to 13 million for Apple Music, which launched in June, 2015.