Parkinson’s disease (PD) is a progressive disorder with a presymptomatic interval; that is, there is a period during which the pathologic process has begun, but motor signs required for the clinical diagnosis are absent. There is considerable interest in discovering markers to diagnose this preclinical stage. Current predictive marker development stems mainly from two principles; first, that pathologic processes occur in lower brainstem regions before substantia nigra involvement and second, that redundancy and compensatory responses cause symptoms to emerge only after advanced degeneration. Decreased olfaction has recently been demonstrated to predict PD in prospective pathologic studies, although the lead time may be relatively short and the positive predictive value and specificity are low. Screening patients for depression and personality changes, autonomic symptoms, subtle motor dysfunction on quantitative testing, sleepiness and insomnia are other potential simple markers. More invasive measures such as detailed autonomic testing, cardiac MIBG-scintigraphy, transcranial ultrasound, and dopaminergic functional imaging may be especially useful in those at high risk or for further defining risk in those identified through primary screening. Despite intriguing leads, direct testing of preclinical markers has been limited, mainly because there is no reliable way to identify preclinical disease. Idiopathic RBD is characterized by loss of normal atonia with REM sleep. Approximately 50% of affected individuals will develop PD or dementia within 10 years. Dataset Link: #machinelearning #artificialintelligence #ai #datascience #python #programming #technology #deeplearning #coding #bigdata #computerscience #tech #data #pythonprogramming #programmer #developer #dataanalytics #software #datascientist #javascript #iot #java #coder #ml #innovation #robotics #linux #analytics [More]
Stuart Russell is a Professor of Computer Science at the University of California and an author. Programming machines to do what we want them to is a challenge. The consequences of getting this wrong become very grave if that machine is superintelligent with essentially limitless resources and no regard for humanity’s wellbeing. Stuart literally wrote the textbook on Artificial Intelligence which is now used in hundreds of countries, so hopefully he’s got an answer to perhaps the most important question of this century. Expect to learn how artificial intelligence systems have already manipulated your preferences to make you more predictable, why social media companies genuinely don’t know what their own algorithms are doing, why our reliance on machines can be a weakness, Stuart’s better solution for giving machines goals, what the future of artificial intelligence holds and much more… Sponsors: Get 20% discount on the highest quality CBD Products from Pure Sport at (use code: MW20) Get perfect teeth 70% cheaper than other invisible aligners from DW Aligners at Extra Stuff: Buy Human Compatible – Get my free Reading List of 100 books to read before you die → To support me on Patreon (thank you): #artificialintelligence #controlproblem #computerscience – 00:00 Intro 00:33 King Midas & AI 06:07 Super-intelligent AI 11:48 Language Challenges 21:42 How AI Could Go Wrong 46:17 Social Media Algorithms 1:03:14 Becoming Enfeebled by Machines 1:20:44 Maintaining Control of AI Growth 1:42:23 Impacts of Stuart’s Work 1:48:01 Where to Find Stuart – [More]
This video is about the Problem with AI job interviews. Some companies providing such solutions claim these AI tools eliminate human bias in the process, but that claim is very questionable. Join the Rethink.Community: Subscribe to be the 1st to receive event updates:​ Objective or Biased by ⏰TIMESTAMPS⏰ 00:00​​ – Intro: AI “Gaydar” 01:00​​ – Why is there Bias in AI? 01:31​​ – Why is AI bias a problem? 02:06 – Personal example on “selection bias” 03:30​ – Everyone is biased 04:46​ – journalists’ test on the AI interview product 06:09 – Another possibly harmful application Subscribe to see more videos like this:​​ Watch my most recent upload: #AI #artificialintelligence #aiinterview ——————————————————————————- RECOMMENDED PLAYLISTS ——————————————————————————- Digital Marketing for Humans:…​ Business Strategy:…​ Humans & Technology:…​ ——————————————————————————- FOLLOW ME ——————————————————————————- Website:​​ Twitter:​​ Instagram: LinkedIn:
Easily fix utorrent connecting to peers problem (6 solutions) to fix the problem on your utorrent client app. The utorrent version used on the video is utorrent 3.5.5 however the solutions demonstrated will work to any utorrent versions. Resources used in the video: Torrent Trackers – Get qBittorrent – ======================================================== * Background Music License * Artist Name: The Spacies Song Name: Heartbeat (Instrumental) License #: 3469305224 ======================================================== * Donation Link * Support by donating any amount! – * Social links * – NEW! Webite: – YouTube: – YouTube (new): – Facebook: ======================================================== Don’t miss an awesome tip, trick or solution to a problem on your pc! Support by leaving a like, comment and subscribe for more helpful tutorials!
In the early 20th century, the US saw a period of extraordinary growth, but also extraordinary inequality. This was the Gilded Age. And although that inequality was a result of a number of forces, it was also a product of technological change and automation of the time. Today, we are in the midst of a massive shift towards a greater reliance on digital technology, and artificial intelligence seems to promise another wave of disruption. What does that future have in store for the gap between in America’s rich and poor? How will AI affect that gap? And why should we care? Julian Jacobs is a recent graduate of Brown University and a Fulbright Scholar whose research has focused on the effects of artificial intelligence on income inequality, the future of work, and American democracy. He is the author of 35 articles and essays in 15 different publications, including interviews with Bernie Sanders, David Cameron, Noam Chomsky, Peter Hitchens, Tom Perez, and Reza Aslan. He is the Founding Editor-In-Chief of the Brown University Journal of Philosophy, Politics, and Economics. Julian previously worked at The Brookings Institution and in President Barack Obama’s post-presidency office, where he supported the 44th President’s correspondence, communications, and speechwriting teams. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at
◄ Language English – 720p – Cortana problems ◄ Click HERE ! – Hit the like button ! ~Recorded by Malasuerte94. Enjoy ! Cortana problems – setting problem – speech recognition [FIX] You are here because you have problems with Cortana on Windows 10 Check the video. Skype: ene_catalin_94 mitroiu_adrian || [LOG] || Malasuerte94 Oracle
Listen to the Science Salon Podcast # 118 (audio-only): In the popular imagination, superhuman artificial intelligence is an approaching tidal wave that threatens not just jobs and human relationships, but civilization itself. Conflict between humans and machines is seen as inevitable and its outcome all too predictable. In this groundbreaking book, distinguished AI researcher Stuart Russell argues that this scenario can be avoided, but only if we rethink AI from the ground up. Russell begins by exploring the idea of intelligence in humans and in machines. He describes the near-term benefits we can expect, from intelligent personal assistants to vastly accelerated scientific research, and outlines the AI breakthroughs that still have to happen before we reach superhuman AI. He also spells out the ways humans are already finding to misuse AI, from lethal autonomous weapons to viral sabotage. If the predicted breakthroughs occur and superhuman AI emerges, we will have created entities far more powerful than ourselves. How can we ensure they never, ever, have power over us? Russell suggests that we can rebuild AI on a new foundation, according to which machines are designed to be inherently uncertain about the human preferences they are required to satisfy. Such machines would be humble, altruistic, and committed to pursue our objectives, not theirs. This new foundation would allow us to create machines that are provably deferential and provably beneficial. Shermer and Russell also discuss: • natural intelligence vs. artificial intelligence • “g” in human intelligence vs. G in AGI (Artificial [More]
Can computers think like humans? Can they learn like us? $1 trillion is spent yearly on tasks a general artificial intelligence can do. D Scott Phoenix of AI startup Vicarious is building exactly that. SCOTT’S HISTORY 00:00 Intro 00:59 How Garry and Scott met 02:02 How Scott came up with the idea to work on AGI ON ARTIFICIAL GENERAL INTELLIGENCE 02:41 The time to build AGI is now 03:10 Why work on AGI? 04:26 What are the building blocks to building a general AI? 04:49 What is a human-like learning system? 06:15 Vicarious vs Deep Learning 08:08 Traditional AI methods resemble insectoid or reptilian brain approaches 09:43 New methods and models are more important than more money on training existing models 11:52 Limits of narrow AI 12:48 History and origins of the AI debate in philosophy and neuroscience 14:45 Brute force methods require 14,000 years of training to do what children only need 2 years to learn 15:28 Lessons from biology 16:24 How do systems layer to generate more complex behavior? 17:30 Is an ambitious project like AGI composable and iterable like SaaS software? ON VICARIOUS 20:01 Long term ambition is great, but what do you do along the way? 20:38 Vicarious’s first applied use case in robotics 22:16 Vicarious vs other robotics approaches 23:47 Building learning systems, not one-off point solutions FOR FUTURE FOUNDERS 24:51 Advice for builders just starting out 25:17 How to tackle large problems and ambitious projects 26:57 Technology is the ultimate lever for humans to [More]
The Learning Problem – Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 of 18 of Caltech’s Machine Learning Course – CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App – and on the course website – Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, This lecture was recorded on April 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
We think that machines can be objective because they don’t worry about human emotion. Even though that’s the case, AI (artificial intelligence) systems may show bias because of the data that is used to train them. We have to be aware of this and correct for it.
When we think about ethics and AI, our first thoughts often go to the question of what happens if a self-driving car kills a pedestrian. The life and death questions of autonomous systems are important to address—and so are other enormous questions like unfair bias, privacy, safety, and accountability. These aren’t just technical or policy questions: they’re design questions. Designers frame problems, shape the features and behavior of AI-enabled systems, and provide the levers that AI is allowed to pull. Designers are in a unique position to help with these problems about AI, design, and ethics, and in this talk, we’ll look at how. WPTV link:
In this episode of Talks at GS, computer scientist Stuart J. Russell discusses how artificial intelligence is shaping both the future of innovation and the future of work and his views on how society can adapt to those changes. Learn More
This is a clip from a conversation with Stuart Russell from Dec 2018. Check out Stuart’s new book on this topic “Human Compatible”: New full episodes every Mon & Thu and 1-2 new clips or a new non-podcast video on all other days. You can watch the full conversation here: (more links below) Podcast full episodes playlist: Podcasts clips playlist: Podcast website: Podcast on iTunes: Podcast on Spotify: Podcast RSS: Note: I select clips with insights from these much longer conversation with the hope of helping make these ideas more accessible and discoverable. Ultimately, this podcast is a small side hobby for me with the goal of sharing and discussing ideas. For now, I post a few clips every Tue & Fri. I did a poll and 92% of people either liked or loved the posting of daily clips, 2% were indifferent, and 6% hated it, some suggesting that I post them on a separate YouTube channel. I hear the 6% and partially agree, so am torn about the whole thing. I tried creating a separate clips channel but the YouTube algorithm makes it very difficult for that channel to grow unless the main channel is already very popular. So for a little while, I’ll keep posting clips on the main channel. I ask for your patience and to see these clips as supporting the dissemination of knowledge contained in nuanced discussion. If you enjoy it, consider subscribing, sharing, and commenting. Stuart Russell [More]