To comprehensively study, understand and inform policy around these complex systems, the next generation of researchers in the physical, social and biological sciences will need fluency with data analysis methods that transverse traditional academic boundaries. A new interdisciplinary curriculum will train graduate students from geosciences, economics, computer science, public policy and other programs in computational and data science techniques critical for modern science. The program will build upon successful UChicago training initiatives such as the Executive Program in Applied Data Analytics, the Computational Analysis and Public Policy curriculum at the Harris School of Public Policy and the Data Science for Social Good Summer Fellowship. Instruction and mentorship will be provided by several UChicago research groups, including the Center for Robust Decision-Making on Climate and Energy Policy (climate and agricultural modeling), Knowledge Lab (text mining), the Energy Policy Institute at UChicago (environmental and energy economics), the Center for Data Science and Public Policy (data analytics and project management) and the Center for Spatial Data Science (spatial analysis).
The Pixel and Pixel XL were premium smartphones aimed directly at Apple's iPhone and were manufactured by HTC for Google, with no branding or mention of HTC on the product or packaging. Thomas Husson, Vice President and principal analyst for Forrester, said: "Two weeks ahead of the likely announcement of new Pixel smartphones and other emerging hardware devices, HTC's acquisition illustrates Google's commitment to the consumer device space. Faced with the potential for a changing landscape where Android is no longer as effective a delivery system for Google services, the decision to produce its own hardware starts to make sense. That requires deeper involvement in hardware" Whether it's a speaker, a smartphone or a computer, in an increasingly competitive landscape, Google needs much better integration between hardware and software if its services are to continue to thrive.
Waymo--the Google self-driving project that spun out to become a business under Alphabet--said Monday it's using Intel chips as part of a compute platform that allows its self-driving Chrysler Pacifica hybrid minivans to process huge amounts of data so it can make decisions in real time while navigating city streets. "As the most advanced vehicles on the road today, our self-driving cars require the highest-performance computers to make safe driving decisions in real time," Waymo CEO John Krafcik said in an emailed statement. However, it wasn't until Waymo started the Chrysler Pacifica minivan project that it began working more closely with the chipmaker. "By working closely with Waymo, Intel can offer Waymo's fleet of vehicles the advanced processing power required for level 4 and 5 autonomy."
With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-4, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. Join Cloud Expo @ThingsExpo conference chair Roger Strukhoff (@IoT2040), October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, for three days of intense Enterprise Cloud and'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets. Accordingly, attendees at the upcoming 21st Cloud Expo @ThingsExpo October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track.
About this course: Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems. In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms.
Availability of massive volumes of data, relatively inexpensive computational capabilities and improved training techniques, such as deep learning, have led to significant leaps in AI capabilities and will only continue to do so for the foreseeable future. Prof. Cambria defined AI 1.0 as logic-based, symbolic (human-readable) AI, which involved creating a model of reality and creating ad-hoc rules in the form of if-then rules or search trees or ontologies (s a formal naming and definition of the types, properties, and interrelationships of the entities that really or fundamentally exist, used to limit complexity and to organise information). Philip Heah, Senior Director (Technology & Infrastructure Group), Infocomm Media Development Authority (IMDA) talked about exploring AI for data centre cooling. Sensors were placed in NSCC's data centres and unsupervised learning techniques (a type of machine learning algorithm used to draw inferences or find hidden patterns from datasets consisting of input data without labelled responses) were employed on the data.
You will explore the main features and capabilities of TensorFlow such as a computation graph, data model, programming model, and TensorBoard. He is also the author of the book Building Machine Learning Projects with TensorFlow, Packt Publishing. Rezaul Karim has more than 8 years of experience in the area of research and development with a solid knowledge of algorithms and data structures in C/C, Java, Scala, R, and Python, focusing on Big Data technologies such as Spark, Kafka, DC/OS, Docker, Mesos, Zeppelin, Hadoop, and MapReduce, and deep learning technologies such as TensorFlow, DeepLearning4j, and H2O-Sparking Water. His research interests include machine learning, deep learning, semantic web/linked data, Big Data, and bioinformatics.
Unlike AI which seeks to understand the world through conceptual models, machine learning has no such interest. The underlying hypothesis of machine learning as applied to log files is that correlation can serve as a proxy for causation. The class of questions for which an answer can be verified in polynomial time is called NP, which stands for "nondeterministic polynomial time." AI emulates human intelligence and is P. Machine learning simulates it and is NP.
One generalized definition might be that smart cities represent the intersection of the Internet of Things (IOT) and analytics – and increasingly, AI – with public infrastructure, public services and city life, in pursuit of whichever of the above objectives the city may have adopted. Once again one is forced into a generalization: AI may come from existing models and tools but it increasingly comes from computing technologies that integrate vast amounts of structured and unstructured data, identify patterns, reason about what they have identified, learn and improve their conclusions with experience, and in some cases interact with humans in natural language. Something has to analyze that data: it is increasingly clear that AI will be that something, enabling multiple systems to be optimized together, detecting emergent patterns, and providing wholly new capabilities in ways that traditional analytics tools cannot. In part, this will be a case of AI taking the blame for the political climate that led to the top down approach in the first place, but fairly or not it could stymie the adoption of AI and smart city technologies generally.