In our SERIES FINALE of Crash Course Computer Science we take a look towards the future! In the past 70 years electronic computing has fundamentally changed how we live our lives, and we believe it’s just getting started. From ubiquitous computing, artificial intelligence, and self-driving cars to brain computer interfaces, wearable computers, and maybe even the singularity there is so much amazing potential on the horizon. Of course there is also room for peril with the rise of artificial intelligence and more immediate displacement of much of the workforce through automation. It’s tough to predict how it will all shake out, but it’s our hope that this series has inspired you to take part in shaping that future. Thank you so much for watching. Produced in collaboration with PBS Digital Studios: Want to know more about Carrie Anne? The Latest from PBS Digital Studios: Want to find Crash Course elsewhere on the internet? Facebook –… Twitter – Tumblr – Support Crash Course on Patreon: CC Kids:
Today’s guest is Matt Zeiler, Founder, and CEO of Clarifai. Clarifai is one of the first startups to apply modern deep learning for image recognition. Their tools are currently used by clients like Staples, OpenTable, and the US Department of Defense (DoD). In this episode, Matt sheds light on the company’s founding story and how an internship at Google was the catalyst for the creation of Clarifai. He also talks about what it’s like competing against industry giants like Facebook and Google. Clarifai’s algorithm and their ability to collaborate rather than compete with their clients truly sets them apart. Matt also sheds light on the benefits of the network effect for both the customers and the company. Matt talks about some of the interesting use cases of their technology, like Trivago, which uses image recognition to organize hotel photos or how the DoD uses it in natural disaster recovery. Matt believes that AI is a great service to the government in helping citizens in many ways, and Clarifai is incredibly proud to be a partner. Tune in to learn more about AI image recognition and be inspired by Matt. Key Points From This Episode -Learn more about Matt’s background, his Ph.D., and what ultimately led him to start Clarifai -Seven years on: Clarifai’s products, their customers and how they use the products -Some of the other applications of AI that Clarifai is potentially interested in getting into -How Clarifai aims to gain a competitive advantage -Why the API model works [More]
Great Learning cordially invites you to be a part of the online launch event of IIIT-Delhi’s Post Graduate Diploma in Computer Science and Artificial Intelligence. The program will be launched in the presence of IIIT Delhi and Great Learning dignitaries, who’ll be sharing their valuable insights on how this program focussed on emerging technologies will help shape our futures. The panelists include: 𝗜𝗜𝗜𝗧-𝗗𝗲𝗹𝗵𝗶 𝗙𝗮𝗰𝘂𝗹𝘁𝘆: ● Prof. Sanjit Krishnan Kaul – Professor, ECE Department, Program Coordinator, PG Diploma in Computer Science & AI, IIIT Delhi ● Prof. Saket Anand – Head, Infosys Center for Artificial Intelligence (CAI), IIIT-Delhi and Associate Professor (CSE, ECE), IIIT-Delhi ● Prof. Raghava Mutharaju – Associate Professor, CSE IIIT Delhi 𝗚𝗿𝗲𝗮𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗧𝗲𝗮𝗺: ● Harish Subramanian – Director, Academics & New Products, Great Learning ● Mohan Lakhamraju – Founder & CEO, Great Learning Key questions that will get answered: How would the program help candidates become future-ready and build rewarding careers? Who is this program for? What roles can participants aspire for? What are the key learning outcomes from the programs?” About Great Learning: – Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more.
Happy Passover wishing you the robots of the R&D Institute for Intelligent Robotic Systems CS Department, College of Management Academic Studies, ISRAEL. This institute was funded by the parents of the three IDF soldiers, in memory of their sons, Benny Avraham, Adi Avitan, and Omer Souad, kidnapped and murdered by Hezbollah in 2000. The Institute was inaugurated in June 2008, and is currently engaged in developing autonomous robotic systems to identify and handle suspicious objects as well as in developing autonomous robotic mapping, delivery and entertainment systems. Background music: Ma Nishtana. Lyrics from the Haggadah, traditional music. Performed by Tom Rahav and Matan Ariel. Musical Arrangement: Noam Zlatin. Recording: Esta Studios, February 2002. The use of the recording for this specific video is authorized by Matan Ariel & Friends ( מוזיקת רקע: מה נשתנה. מילים מן ההגדה, לחן מסורתי. ביצוע: תום רהב ומתן אריאל. עיבוד מוזיקלי, ניהול מוזיקלי ונגינה: נעם זלטין. הוקלט באולפני אסטה, פברואר 2002. השימוש בהקלטה בווידאו זה באישור מתן אריאל וחברים (
Today we’re going to talk about how computers understand speech and speak themselves. As computers play an increasing role in our daily lives there has been an growing demand for voice user interfaces, but speech is also terribly complicated. Vocabularies are diverse, sentence structures can often dictate the meaning of certain words, and computers also have to deal with accents, mispronunciations, and many common linguistic faux pas. The field of Natural Language Processing, or NLP, attempts to solve these problems, with a number of techniques we’ll discuss today. And even though our virtual assistants like Siri, Alexa, Google Home, Bixby, and Cortana have come a long way from the first speech processing and synthesis models, there is still much room for improvement. Produced in collaboration with PBS Digital Studios: Want to know more about Carrie Anne? The Latest from PBS Digital Studios: Want to find Crash Course elsewhere on the internet? Facebook –… Twitter – Tumblr – Support Crash Course on Patreon: CC Kids:
Human Computer Integration versus Powerful Tools Umer Farooq, Jonathan Grudin, Ben Shneiderman, Pattie Maes, Xiangshi Ren CHI ’17: ACM CHI Conference on Human Factors in Computing Systems Session: Human Computer Integration versus Powerful Tools Abstract In 1960, JCR Licklider forecast three phases for how humans relate to machines: human-computer interaction, human-computer symbiosis, and ultra-intelligent machines. Have we moved from interaction to symbiosis or integration, should we focus on this or on other aspects of human augmentation via powerful tools, and how will such decisions affect us as designers, researchers, and members of society? This panel will raise uneasy and disruptive HCI notions. For example, we will debate whether integration is a necessary and desirable next phase, or whether it could undermine human self-efficacy and control and lessen the predictability of machine actions. DOI:: WEB:: Recorded at the ACM CHI Conference on Human Factors in Computing Systems in Denver, CO, USA May 6-11, 2017
Computer AI (Artificial Intelligence) and what could go wrong. August Bradley’s guest is Chris Paine, director of the AI documentary film “Do You Trust This Computer?” and previously the documentary “Who Killed the Electric Car?”. The new film is a powerful examination of artificial intelligence centered around insights from the most high-profile thinkers on the subject, including Elon Musk, Stuart Russell, Max Tegmark, Ray Kurzweil, Andrew Ng, Westworld creator Jonathan Nolan and many more. Chris set out to ask these leaders in the field “what scares smart people about AI”, and they did not hold back. Subscribe to the MIND & MACHINE future-tech newsletter: Podcast Audio version at: More on Chris and the Film: Chris Paine’s Production Company, Papercut Films: “Do You Trust This Computer?” Website: __________ MIND & MACHINE features interviews by August Bradley with bold thinkers and leaders in transformational technologies. Subscribe to the MIND & MACHINE newsletter: MIND & MACHINE Website: Subscribe to the podcast on: iTunes: Overcast: Android or Other Apps: Show Host August Bradley on Twitter:
Geordie Rose, Founder and CTO of D-Wave Systems describes some of the challenges the team had to overcome in building the first commercial quantum computer. Learn more about D-Wave and the first commercial quantum computers at
All Elon Musk clips from Documentary “Do You Trust This Computer” by Chris Paine.
Re-Upload of some years old video. Geordie Rose – Quantum Computing: Artificial Intelligence Is Here Caitlyn Jenner Shapeshifting: Safe and Sorry – Terrorism & Mass Surveillance By: Kurzgesagt – In a Nutshell BLADE RUNNER 2049 – Trailer 2 By: Warner Bros. Pictures Signs in the Heavens That Google Sky Was Hiding, Red Dragon of Revelation, September 23rd By: DAHBOO77 Neurons and What They Do ~ An Animated Guide Music: The Cinematic Orchestra Arrival of the Birds & Transformation From: ThinkPinkR the answer to life, universe and everything From: riktw Back to the Roots (Official Video) By: NEWARTSTUDiOS
This is the direction of the future. Useful AI that can do the research of a thoudand men instantly. It’s definitely worth noting that Watson is capable of learning (a point I didn’t touch on in this video), so what you see here is the “baby phase” so to speak. I tried to leave out the technical jargon in this video but for those who want to know more, a wiki dump on Watson is below: According to John Rennie, Watson can process 500 gigabytes, the equivalent of a million books, per second. Software Watson uses IBM’s DeepQA software and the Apache UIMA (Unstructured Information Management Architecture) framework. Hardware The system is workload optimized, integrating massively parallel POWER7 processors and being built on IBM’s DeepQA technology, which it uses to generate hypotheses, gather massive evidence, and analyze data. Watson is composed of a cluster of ninety IBM Power 750 servers, each of which uses a 3.5 GHz POWER7 eight core processor, with four threads per core. In total, the system has 2,880 POWER7 processor cores and has 16 terabytes of RAM. How Watson Worked on “Jeopardy!”: Soundtrack: TCTS – You Faux Tales- Atlas Plan – Giga giga Winter Flags – Winter Flags Maths Time Joy – Walk With Me Ruddyp x Taquwami – Hold More Music: » Google + | » Facebook | » Patreon | » My music | or » » Twitter | @Coldfustion
GEORDIE ROSE (CEO OF D-WAVE QUANTUM COMPUTERS) SAID, “Nobody is paying attention. This A.I. construct is happening in the background while the bickering continues on politics and healthcare. As a metaphor, think if an alien race sent a message to earth that it would be here in 10 years. The entire world would pool resources to figure out how to stop it. Well, A.I. is just like that, it’s coming quickly. These “Alien Beings” that we’re creating are not gonna be like us. They’ll be like those ‘Aliens’ and way more intelligent. H.P. Lovecraft espoused this view of ‘cosmicism’ (cosmic indifference). Essentially, as a metaphor for A.I., there are more advanced ‘entities’ out there that are neither good nor evil, but they don’t give a sh!t about you in the slightest. The same way you don’t care about an ant, is how they don’t care about you.” He continues, “On that pleasant note, we’re hiring people to try to make something like this happen. I’m sort of a bit “tongue in cheek” about all this because it’s really agnostic. Technology can be used for good or for evil. If you want to a say in how this all goes down, you can’t sit on the sidelines, you have to get involved. Because the code that you write may be in use in these ‘A.I. entities’ in the next 10 years.” —- THAT NEW AGE MANTRA “WE ARE ALL ONE” IS DIGITALLY COMING TRUE Elon Musk said, “We are unequivocally headed [More]
Interview with Stuart Russell, Professor of Computer Science, University of California, Berkeley, at the AI for Good Global Summit 2018, ITU, Geneva, Switzerland.
This is the second part of Rose’s speech at the debut of a 16 qubit computation system quantum singularity computer qubit Here is some video of the D:Wave quantum computer running an application. . A quantum computer is a device for computation that makes direct use of quantum mechanical phenomena, such as superposition and entanglement quantum singularity computer qubit
“Human-compatible AI” by Stuart Russell, Professor of Computer Science at UC Berkeley on March 21st 2018 at the Data Driven Paris. Did you enjoy this conference? You can like the video and subscribe to our channel 👉 Do not hesitate to add a comment, we will be happy to answer them 😊 Follow Serena on 👇 Website ► LinkedIn ► Twitter ► Medium ►
Is The New 2019 Google Quantum Computer Sign Of End Times? Googles New Computer Does 10,000 Years Of Calculations In Three Minutes. Is Googles New Quantum Computer Causing The Mandela Effect? Subscribe for more: Share this video with a friend: Check out the complete Simulation Theory playlist here: My Video On The Mandela Effect: My Synchronicity Video: Welcome to Open Your Reality. I’m Chad, your host. Folks, we’re coming into interesting times. I came across a news article today on CNN and the NY Times stating quantum supremacy has finally been achieved – and by none other than google. If you didn’t know, quantum supremacy was a term coined in 2012 by physicist Jim Preskill, which means that the supremacy of quantum computers is now becoming a reality. Certainly, many companies, and even world governments, most notably China, have been hard at work over the last two decades trying to perfect the quantum computer. The new space race or arms race is actually the race to build the perfect quantum computer? Why? Because artificial intelligence powered by computer technology will become the most powerful weapon in the 21st century, and quantum computers are the key to it all. But there’s other stuff they can do too, which I’ll get into later in the video. So now Google has a new quantum computer that can far outpace any ordinary computing technology that exists today. Apparently, Google claims their new quantum computer can perform 10,000 years of [More]
The Ancient Secrets of Computer Vision An introductory course on computer vision originally held Spring 2018 at the University of Washington.
Are humans inherently better than machines? Or do computers have the advantage on us when it comes to intelligence? Code Play Teach Pro Tip: Alt+16 = ► Alt+1 = ☺
Though the field of artificially intelligent computers is exploding, not all scientists promise great things; some warn this could be mankind’s greatest disaster. Films portray a future where computers become smarter than humanity, developing their own ideas about how to implement their programming based on what they think is most beneficial for everyone, including themselves. Because that possibility actually exists, scientists are developing moral guidelines for them. From private foundations to DARPA, everyone’s in the game. Whose moral ideas will prevail? Judeo-Christian civilization bases its morality on God’s Word. But those working on AI morals don’t have the same foundation; they’re mostly relativists. And what if machines decide they’re intelligent enough to create their own standards? Transhumanist Zoltan Istvan sees this as natural, but his overwhelming desire for immortality through technology makes him overly optimistic. Current work revolves around teaching machines how to understand language nuances, adopt human standards from that, and place them in context for relevant situations. But they’re doomed to failure because human-based morality always is relative. Basing a computer’s morality on what it can learn from man will bring disaster, because the heart of man invariably is deceptive and selfish. Scientist Elon Musk warns, “With artificial intelligence we’re summoning the demon. You know those stories where there’s the guy with the pentagram, and the holy water, and … he’s sure he can control the demon? Doesn’t work out.” It seems he’s right. An exchange between Google programmers and an AI computer shows that, even at this [More]
Google Tech Talks December, 13 2007 ABSTRACT This tech talk series explores the enormous opportunities afforded by the emerging field of quantum computing. The exploitation of quantum phenomena not only offers tremendous speed-ups for important algorithms but may also prove key to achieving genuine synthetic intelligence. We argue that understanding higher brain function requires references to quantum mechanics as well. These talks look at the topic of quantum computing from mathematical, engineering and neurobiological perspectives, and we attempt to present the material so that the base concepts can be understood by listeners with no background in quantum physics. In this second talk, we make the case that machine learning and pattern recognition are problem domains well-suited to be handled by quantum routines. We introduce the adiabatic model of quantum computing and discuss how it deals more favorably with decoherence than the gate model. Adiabatic quantum computing can be understood as an annealing process that outperforms classical approaches to optimization by taking advantage of quantum tunneling. We also discuss the only large-scale adiabatic quantum hardware that exists today, built by D-Wave. We present detailed theoretical and experimental evidence showing that the D-Wave chip does indeed operate in a quantum regime. We report about an object recognition system we designed using the adiabatic quantum computer. Our system uses a combination of processing steps, where some are executed on classical hardware while others take advantage of the quantum chip. Both interest point selection and feature extraction are accomplished using classical filter operations reminiscent [More]
So we’ve talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever. Produced in collaboration with PBS Digital Studios: Want to know more about Carrie Anne? The Latest from PBS Digital Studios: Want to find Crash Course elsewhere on the internet? Facebook –… Twitter – Tumblr – Support Crash Course on Patreon: CC Kids:
Let’s separate the hype from reality and see what exactly machine learning (ML), deep learning (DL) and artificial intelligence (AI) algorithms can do right now in cybersecurity. We will look how different tasks, such as prediction, classification, clustering and recommendation, are applicable to the ones for attackers, such as captcha bypass and phishing, and for defenders, such as anomaly detection and attack protection. Speaking about the icing on the cake, we will cover the latest techniques of hacking security and non-security products that use ML and why its super hard to protect them against adversarial examples and other attacks. === Alexander is a co-founder of ERPScan, the president of, an organization focused on enterprise application security, and a member of Forbes Technology Council. He has been recognized as R&D Professional of the Year by 2013. His expertise covers the security of enterprise business-critical software and includes ERP, industry-specific solutions and adopting Machine Learning and Deep learning inventions to cybersecurity problems. He has presented his research at over 100 conferences such as BlackHat, HITB, RSA held in more than 20 countries in all continents. He has held customized trainings for CISOs of Fortune 2000 companies.
I put the word “evolve” in there because you guys like “evolution” videos, but this computer is actually learning with gradient descent! All music in this video is either by Bach, Mozart, or Computery. GizmoDude8128 wins a prize for figuring out that 100101 in base 2 is 37 in base 10 the fastest! (Question inspired by fixylol) Andrej Karpathy’s blog post on RNNs: