It seems like AI, data science, machine learning and bots are some of the most discussed topics in tech today. My preferred way to do this is always through listening to podcasts. Here are the ones I've found the most interesting: They alternate between great interviews with academics & practitioners and short 10–15 minute episodes where the hosts give a short primer on topics like calculating feature importance, k-means clustering, natural language processing and decision trees, often using analogies related to their pet parrot, Yoshi. This is the only place where you'll learn about k-means clustering via placement of parrot droppings. Hosted by Katie Malone and Ben Jaffe, this weekly podcast covers diverse topics in data science and machine learning: talking about specific concepts like model theft and the cold start problem and how they apply to real-world problems and datasets.

He contributed to Mahout clustering, classification and matrix decomposition algorithms and helped expand the new version of Mahout Math library. Ted was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems, he built fraud detection systems for ID Analytics (LifeLock) and he has issued 24 patents to date. Ted has a PhD in computing science from University of Sheffield. When he's not doing data science, he plays guitar and mandolin. Ellen Friedman is a consultant and commentator, currently writing mainly about big data topics.

Wouldn't it be fabulous if Salesforce, Microsoft, Google, and other software companies could make software smart enough to straighten out the tangle of features-and-functions they've been selling to us for the last 20 years? While none of those companies would likely describe what they're doing in those terms, they are all pushing artificial intelligence (AI) technology that they claim helps software anticipate a user's needs based on human-computer interactions and the data users deal with every day. Salesforce crm, for example, says it is enabling all of its sales, marketing, e-commerce, and other "cloud" software with AI. In theory that means the software would tell a sales rep which prospects are close to signing a purchase order and which are leaning towards a competitor based on website visits, social media posts, email, online demos taken etc. Microsoft msft is likewise adding AI to its Office productivity applications and Dynamics business software.

A 14-year-old girl who wanted her body to be preserved, in case she could be cured in the future, won a historic legal fight shortly before her death. The girl, who was terminally ill with a rare cancer, was supported by her mother in her wish to be cryogenically preserved -- but not by her father. She wrote to the judge explaining that she wanted "to live longer" and did not want "to be buried underground". The girl, who died in October, has been taken to the US and preserved there. Bionic Eyes Are Coming, and They'll Make Us Superhuman Would you like to upgrade your eyes?

In the TV show --Westworld,-- artificially intelligent (AI) robots built to look and act exactly like humans take guests through different narratives of an old-west-styled amusement park in order to help them live out their fantasies -- whether promiscuous, adventurous, or deadly. In episode two of the show, one robot--s glitch gives --Westworld-- employees pause, causing them to decommission it. A scientist in the programming division suggests this glitch may be contagious, --so to speak,-- and could infect other robots, or --hosts-- as they call them in the show. This theory is denied by her superior, but later in the episode, the daughter of the decommissioned robot is shown seemingly infecting another host with "consciousness" by repeating what could--be a trigger phrase:--"These violent delights have violent ends." All of this seems like an eerie (and perhaps, impossible) peek into the future, but Christopher Atkeson, a professor at the Robotics Institute and Human-Computer Interaction Institute at Carnegie Mellon University, told INSIDER that simulated consciousness already exists in products such as Amazon Echo and the iPhone--s Siri, and that AI systems infecting other AI systems is not a hypothetical scenario.

A Deep Neural Network (DNN) is an artificial neural network (ANN) with multiple hidden layers of units between the input and output layers. Similar to shallow ANNs, DNNs can model complex non-linear relationships. DNN architectures (e.g. for object detection and parsing) generate compositional models where the object is expressed as a layered composition of image primitives. The extra layers enable composition of features from lower layers, giving the potential of modeling complex data with fewer units than a similarly performing shallow network. DNNs are typically designed as feedforward networks, but research has very successfully applied recurrent neural networks, especially LSTM, for applications such as language modeling. Convolutional deep neural networks (CNNs) are used in computer vision where their success is well-documented. CNNs also have been applied to acoustic modeling for automatic speech recognition, where they have shown success over previous models.

Recent breakthroughs in artificial intelligence and machine learning are enabling computers to understand the world and respond intelligently to it. Google is already embracing these technologies for Android, but they're poised to have bigger implications, touching everything from drones to medical diagnosis. He made his fortune as co-founder of Netscape two decades ago, and more recently his firm has invested in successful companies like Facebook, Twitter, Airbnb, Slack, and Lyft. Andreessen is in constant contact with entrepreneurs and investors trying to build the next great technology company. Andreessen argues that recent breakthroughs mean artificial intelligence has the potential to spawn a new generation of big, important technology companies. At the same time, he acknowledges that certain industries have proven stubbornly resistant to technological change -- and he argues that more work is needed to bring the power of software to every corner of the economy. We spoke by phone in late September. The transcript has been edited for length and clarity.

Recent advances in Natural Language Processing and Machine Learning provide us with the tools to build predictive models that can be used to unveil patterns driving judicial decisions. This can be useful, for both lawyers and judges, as an assisting tool to rapidly identify cases and extract patterns which lead to certain decisions. This paper presents the first systematic study on predicting the outcome of cases tried by the European Court of Human Rights based solely on textual content. We formulate a binary classification task where the input of our classifiers is the textual content extracted from a case and the target output is the actual judgment as to whether there has been a violation of an article of the convention of human rights. Textual information is represented using contiguous word sequences, i.e.

Robots have become commonplace in many aspects of life including health care, military and security work. Yet until recently little thought has been given outside of academic circles to the ethics of robots. Silicon Valley Robotics recently launched a Good Robot Design Council -- which has launched "5 Laws of Robotics" guidelines for roboticists and academics -- on the ethical creation, marketing and use of robots in everyday life. Robots should comply with existing law, including privacy. Robots are products; they should be safe, reliable and not misrepresent their capabilities.

These techniques, when applied correctly, can help assist in many data driven tasks to provide interesting insights and decision recommendations to analyst. While these techniques can be powerful, for the researchers and analyst who are not well versed in machine learning, there can exist a gap in understanding that may prevent them from looking at and applying these tools to problems machine learning techniques could assist with. The goal of this presentation is to help researchers, analyst, and security enthusiast get their hands dirty applying machine learning to security problems. We will walk the entire pipeline from idea to functioning tool on several diverse security related problems, including offensive and defensive use cases for machine learning. Through these examples and demonstrations, we will be able to explain in a very concrete fashion every step involved to tie in machine learning to the specified problem.