Artificial Intelligence in Everyday Life with specific examples how Artificial Intelligence affect our lives? AI affects our life everyday in many ways behind the scenes. AI improves productivity and enjoyment.
Artificial intelligence has the potential to impact all areas of life and work in the future. What kind of research are large companies such as Google undertaking in the area of artificial intelligence? What impacts will, for instance, new machine learning systems have on health care or industry? When will computers be able to read and understand a book the way humans do? Jeff Dean, Ph.D., who joined Google in 1999, focuses on such questions – and their answers – in his daily work leading Google Research and Google Health.
“Building Intelligent Computer Systems With Large Scale Deep Learning”
In this episode I’m joined by Jeff Dean, Google Senior Fellow and head of the company’s deep learning research team Google Brain, who I had a chance to sit down with last week at the Googleplex in Mountain View.
Artificial Intelligence | 3 REAL reasons WHY it won’t TAKEOVER
October 27, 2011
Stanford Center for Internet and Society
Prof. Edmond Awad
(Institute for Data Science and Artificial Intelligence at the University of Exeter)
A panel at the “Web We Want” festival, May 2015 at the Southbank Centre, London. The video was broadcast via Periscope – hence the quality, and is used by kind permission of the WebWeWant Festival, SouthBank Centre and organisers ANXS Collective
Machine Learning Python Weather Prediction
🔥Edureka and NIT Warangal Post Graduate Program on AI and Machine Learning: https://www.edureka.co/post-graduate/machine-learning-and-ai
This Edureka Session explores and analyses the spread and impact of the novel coronavirus pandemic which has taken the world by storm with its rapid growth. In this session, we shall develop a machine learning model in Python to analyze what has been its impact so far and analyze the outbreak of COVID 19 across various regions, visualize them using charts and tables, and predict the number of upcoming confirmed cases.
Finally, we’ll conclude with a few safety measures that you can take to save yourself and your loved ones from getting adversely affected in the hour of crisis.
02: 53 Introduction to COVID 19
05:49 Case Study: the outbreak of COVID 19
Education and Ethics in an AI World
The advances made in AI and machine learning has fundamentally changed the way businesses operate. Yet the question that everyone keeps asking is how will the future of technology transform the nature of work and the workplace itself?
As a principal scientist at Amazon Web Services, and Bren Professor at CalTech, Anima Anandkumar bridges the gap between theory and practice in artificial intelligence. Here, Anandkumar considers questions on building the next generation of AI. What decisions go into building a machine learning algorithm? What does learning mean in the context of AI?
Dr. Anima Anandkumar, Professor at the California Institute of Technology, delivered a talk titled “Infusing Structure into Machine Learning Algorithms” on March 15, 2019 in Ann Arbor, Michigan as part of the Michigan Institute for Data Science(MIDAS) Seminar Series.
What does Augmented Analytics mean? Augmented analytics, an approach that automates insights using machine learning and natural-language generation, marks the next wave of disruption in the data and analytics market. Data and analytics leaders should plan to adopt augmented analytics as platform capabilities mature. – Gartner
Artificial Intelligence is about to take over the job market. Brian Mullins, founder of DAQRI, thinks Augmented Reality could be the antidote needed to empower the next generation of worker and transform the way we think.
One of the main challenges for AI remains unsupervised learning, at which humans are much better than machines, and which we link to another challenge: bringing deep learning to higher-level cognition. We review earlier work on the notion of learning disentangled representations and deep generative models and propose research directions towards learning of high-level abstractions. This follows the ambitious objective of disentangling the underlying causal factors explaining the observed data. We argue that in order to efficiently capture these, a learning agent can acquire information by acting in the world, moving our research from traditional deep generative models of given datasets to that of autonomous learning or unsupervised reinforcement learning. We propose two priors which could be used by an agent acting in its environment in order to help discover such high-level disentangled representations of abstract concepts. The first one is based on the discovery of independently controllable factors, i.e., in jointly learning policies and representations, such that each of these policies can independently control one aspect of the world (a factor of interest) computed by the representation while keeping the other uncontrolled aspects mostly untouched. This idea naturally brings fore the notions of objects (which are controllable), agents (which control objects) and self. The second prior is called the consciousness prior and is based on the hypothesis that our conscious thoughts are low-dimensional objects with a strong predictive or explanatory power (or are very useful for planning). A conscious thought thus selects a few abstract factors (using the attention mechanism which brings these variables to consciousness) and combines them to make a useful statement or prediction. In addition, the concepts brought to consciousness often correspond to words or short phrases and the thought itself can be transformed (in a lossy way) into a brief linguistic expression, like a sentence. Natural language could thus be used as an additional hint about the abstract representations and disentangled factors which humans have discovered to explain their world. Some conscious thoughts also correspond to the kind of small nugget of knowledge (like a fact or a rule) which have been the main building blocks of classical symbolic AI. This, therefore, raises the interesting possibility of addressing some of the objectives of classical symbolic AI focused on higher-level cognition using the deep learning machinery augmented by the architectural elements necessary to implement conscious thinking about disentangled causal factors.
MIT Introduction to Deep Learning 6.S191: Lecture 8
Algorithmic Bias and Fairness
Lecturer: Ava Soleimany
The Future of Work: Capital Markets, Digital Assets, and the Disruption of Labor
Date: Friday, April 27, 2018
Artificial intelligence might be a technological revolution unlike any other, transforming our homes, our work, our lives; but for many – the poor, minority groups, the people deemed to be expendable – their picture remains the same.
Kate Crawford is a leading researcher, academic and author who has spent the last decade studying the social implications of data systems, machine learning and artificial intelligence. She is a Distinguished Research Professor at New York University, a Principal Researcher at Microsoft Research New York, and a Visiting Professor at the MIT Media Lab.
Audience at Slush 2017 enjoyed keynotes on augmenting human capabilities to new dimensions. Here’s one from Harri Valpola, CEO & Co-Founder at The Curious AI Company.
Watch the 2019 PAIR (People + AI Research) Symposium, which took place live at Google’s Offices in London, UK on November 14, 2019. The focus: Participatory ML (machine learning). Inspired by the participatory design movement, it is an approach to building ML systems that actively involves a diversity of stakeholders (technologists, UXers, policymakers, end users, citizens).
Robots are on the lose in Axle City and it’s up to Blaze and AJ to stop them! Can you help Blaze transform into a robot in order to save the day?
Click here to Subscribe to SET India: https://www.youtube.com/channel/UCpEhnqL0y41EpW2TvWAHD7Q?sub_confirmation=1
How does a group of animals — or cells, for that matter — work together when no one’s in charge? Tiny swarming robots–called Kilobots–work together to tackle tasks in the lab, but what can they teach us about the natural world?
Robots have been used in every aspect of manufacturing for years. But now, they are leaving the factory floor and learning to become better at everyday tasks than humans.
Best 5 Humanoid Robots 2017, You’ll Intend to Buy – Inmoov, EZ Robot, Poppy, Plen 2, Kengoro,
Through innovation, Japan is offering solutions to various challenges that the world faces.
Watch the “INNOVATION JAPAN” series and get inspired.
This week Russell tackles the regular mass panic from the media that technology is advancing to quickly…
Google’s AI AlphaZero has shocked the chess world. Leaning on its deep neural networks, and general reinforcement learning algorithm, DeepMind’s AI Alpha Zero learned to play chess well beyond the skill level of master, besting the 2016 top chess engine Stockfish 8 in a 100-game match. Alpha Zero had 28 wins, 72 draws, and 0 losses. Impressive right? And it took just 4 hours of self-play to reach such a proficiency. What the chess world has witnessed from this historic event is, simply put, mind-blowing! AlphaZero vs Magnus Carlsen anyone? 🙂
In this project I built a neural network and trained it to play Snake using a genetic algorithm.
One of the promise of IoT is to allow bringing the intelligence of the Cloud to the Edge to run IoT data analytics as close as possible to the data source. This allows to reduce latencies, optimize performance and response times, support offline scenario, comply with privacy policies and regulations, reduce data transfer cost, and more…
One thing you really have to consider when bringing Artificial Intelligence to the edge is the hardware you will need to run these powerful algorithms. Ted Way from the Azure Machine Learning team joins Olivier on the IoT Show to discuss hardware acceleration at the Edge for AI. We will discuss scenarios and technologies Microsoft develops and uses to accelerate AI in the Cloud and at the Edge such as Graphic cards, FPGA, CPU,… To illustrate all this, Ted walks us through real life scenarios and demos IoT Edge running Machine Learning vision algorithms.
Learn more about hardware acceleration for AI at the Edge: https://docs.microsoft.com/azure/machine-learning/service/concept-accelerate-with-fpgas
Create a Free Account (Azure): https://aka.ms/aft-iot
NTA UGC NET 2020 (Paper-1) | Information & Communication Technology (ICT) by Aditi Ma’am | Generation of Programming Computer
Welcome to this episode in Natural Language Processing Zero to Hero with TensorFlow. In the previous videos in this series you saw how to tokenize text, and use sequences of tokens to train a neural network. In the next videos we’ll look at how neural networks can generate text and even write poetry, beginning with an introduction to Recurrent Neural Networks (RNNs).