ANJA KASPERSEN: Today I am joined by Joel Rosenthal and Stelios Vassilakis for an irreverently engaging conversation about the impact of artificial intelligence (AI) on democracy, what we can learn from the Athenian agora in preserving what it means to be human in the biodigital realm, and how ethics empower civil engagement. Stelios Vassilakis is co-directing programs and strategic initiatives at the Stavros Niarchos Foundation, which is one of the leading international philanthropic organizations. Stelios is also a classics and modern Greek studies scholar, specializing in the works of Homer. Joel Rosenthal is president of Carnegie Council for Ethics in International Affairs and a distinguished public intellectual of international relations and foreign policy. Before handing the floor over to Joel to guide us through this conversation, I am very curious about these concepts that are guiding the work of both of your institutions. For the Stavros Niarchos Foundation it is empowering humanity, and for Carnegie Council it is about empowering ethics, and obviously there is a strong link between the two. I think in today's world we live in a very distrustful world, a crowded and overheated public space--if we can even identify that space, which we have talked about is a difficult space to even find--and so what we are trying to do at the beginning to empower ethics is first of all just to identify the issues, and to identify these issues, put a name on them, label them, and show them to be issues of competing values and competing interests that would benefit from reflection, dialogue, and discussion, even that question of identification and clarification of these issues and to bring them to the fore in a way that will not necessarily lead to polarization but can lead to constructive dialogue. The second step is to provide thought leadership around these questions--there are people who have dedicated their lives to thinking about some of these issues and to studying these issues; they have great competence and some authority in speaking about these issues--and to identify those people and bring that thought leadership to bear on these questions. Critically, though, it is not just about thinking. It is also about experience. There are people who are actually working on these issues, they are working these problems. It is part of their personal and professional life, and I think that the experience that they have themselves is almost as valuable if not more valuable than those who spend their lives thinking about these issues and creating scholarship around them. So when we talk about thought leadership we're talking about both scholarship and lived experience, Carnegie Council being a place where we can bring that expertise, if you will, to bear on these questions. The third part that is also critical today is to create a community of engagement around these issues.

The reason why you have many misunderstandings about artificial intelligence is mainly because you only see some people's speech, but don't understand the basic principles of AI. This article will help you understand the basic principles of AI. The essence of things is often not what everyone said. We use traditional software to compare with artificial intelligence. It is easier to understand with a frame of reference.

Another one from Uncle Bob, the Single Responsibility Principle dictates that each module is only ever responsible for one thing. To create clear programs and code, the Single Responsibility Principle means for each module to control a single optionality. Your final version of code probably includes main functionality and options. When sharing your code, clearly mark sections of code which should and should not be modified. Open code signals to others that those sections can be modified without breaking the programme, whereas closed code lets others know what not to change.

This lecture discusses how decision trees can be used to represent predictor functions. Variations of the basic decision tree model provide some of the most powerful machine learning methods curren... Alexander Jung uploaded a video 1 week ago Classification Methods - Duration: 46 minutes. Our focus is on linear regression methods which can be expanded by feature constructions. Guest lecture of Prof. Minna Huotilainen on learning processes in human brains. Alexander Jung subscribed to a channel 3 weeks ago Playing For Change - Channel PFC is a movement created to inspire and connect the world through music. The idea for this project came from a common belief that music has the power to break down boundaries and overcome distances SubscribeSubscribedUnsubscribe1.9M This video explains how network Lasso can be used to learn localized linear models that allow "personalized" predictions for individual data points within a network.

The text has been redacted and altered by the BMBF in addition to DW's normal editorial guidelines. As such, the text does not entirely reflect the audio of the interview as recorded on December 5, 2019. DW: We're in Berlin at an "Artificial Intelligence Camp" organized by the Gesellschaft für Informatik and the German Federal Ministry of Education and Research, where you head the department for "Research for Digitalization and Innovation." Artificial intelligence is in your remit. And all the people here are experts in the field.

As an example, consider clustering pixels in an image (or video) if they belong to the same object. Different clustering methods are obtained by using different notions of similarity and different representations of data points.

IDEAS 278,119 views SolidWorks Tutorial #181: Cola Bottle - Duration: 27:34. SolidWorks Tutorial 1,589,749 views Inside a Google data center - Duration: 5:28.

US author Philip K. Dick wrote around 40 novels and 120 short-stories on his writing machine. With this classic tool he created claustrophobic science fiction scenarios, writing less about alien invasions, but artificial intelligence. Doing so, he not only described technical possibilities, but furthermore described ethical concerns. Most of his stories featured different layers of reality, what his work compares to Franz Kafka. As all good science fiction, his stories were less an escape from reality, but the opposite, allowed us a view into a distorted mirror.

What are neural networks, but more important, how are they trained in practice? How can data scientists design an optimal neural network when a single training run can take 2 weeks? In this Data Science Central webinar we will start from the foundation of what deep learning is then fast forward through what it takes to train a production quality neural network. You won't be able to train a network when this talk is over, but you'll understand enough basics to start smart conversations about our customers' practice of deep learning.

Artificial intelligence (AI) might evolve to the point where humans are no longer in control. Facebook was recently forced to shut down an experiment after two artificial intelligence programs began chatting to each other in their own language. Researchers at the Facebook AI Research Lab (FAIR) found the chatbots had deviated from their script and were communicating in a new language developed without human input. The chatbots developed this shorthand as a faster mechanism for negotiating trade and value pricing for objects such as hats, balls and books. While seemingly innocent, concerns over "rogue" AI are valid, as they are examples of how human control can quickly get disintermediated.