Looking at traditional education, the system is focused on ‘teaching’ rather than ‘learning’. This is what Philipp Schmidt, director of learning innovation at the MIT Media Lab says.

Currently, as learners, students do not make their own decisions in terms of where, what and how they choose what to learn and work on.

In this video Schmidt discusses active learning and how to get around some of the challenges in our current educational systems to push students into more creative directions.

Schmidt also talks about artificial intelligence and the role it will play in affecting education systems, in the near future.

Help us caption & translate this video!


What will the school of the future look like? Will artificial intelligence
replace our teachers? How will the education system cope with technological advances? WIll going to school still be necessary? Or will knowledge and studying be completely online?

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Editing: Christian Beller https://www.youtube.com/channel/UCOfMoCMDRAQRVC8AJSvI0EA

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The presentation gives a short overview of the future of ‘Artifical Intelligence in Education.’

Reference and Readings

Thought Leaders Examine the Future of AI in Security, How It Can Be Utilized

AI Bolstering Security through Weapon Detection

AI (artificial intelligence)

Artificial Intelligence (AI)

Exploring the impact of artificial intelligence on teaching and learning in higher education

Carnegie Mellon Dean Of Computer Science On The Future Of AI

What AI have MIT been creating? – BBC Click


Need Teaching Assistance? Artificial Intelligence to the Rescue!

Using Artificial Intelligence As a Teaching Assistant To Help With Questions Online

Digital transformation in higher education: Big data and education intelligence platforms

Understanding the four types of AI, from reactive robots to self-aware beings

Dr. Jowallah’s Qualifications
EdD (Language and Literacy in Education, University of Sheffield UK), Post Graduate Certificate. (Teaching and Learning in Higher Education, University of Wolverhampton), Post Graduate Certificate ( Education Management, The Open University UK), Post Graduate Certificate (Research in Education The Open University UK), M.Ed. (Special Needs and Inclusion, The Open University UK), BSc. ( Psychology, The Open University UK), Dip. Ed (Bethlehem Moravian College, Jamaica and Cert (Social Work, University of the West Indies School of Continuing Studies, Jamaica)

Professor Ben Shneiderman, Department of Computer Science & Human-Computer Interaction Lab, University of Maryland, talks about Human-Centered Artificial Intelligence: Designing Next-Generation User Experiences, during a Distinguished Lecture Series held at the Office of Naval Research.

What does it mean for something to be Artificially Intelligent?

Abi Aryan is the Chief Business Development Officer at Coinsecure – India’s leading Bitcoin exchange. With a background in mathematics, statistics and computer science, she has been at the core of many futuristic companies. Working on Neural Networks and Artificial Intelligence have been one of the most defining moments in her life during her masters degree at London School of Economics as it went on to become her passion. She’s been deeply involved with the Big Data, Singularity and Artificial Intelligence circles in U.K. and has hosted several subject experts at her own conferences with Computability Intelligence Unconference and Future Tech Track in London.

This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx

George, overly influenced by Star Trek, previously worked at NASA, earning his “rocket scientist” credentials. He currently manages Rocket Fuel and has married big data with artificial intelligence to create the leading programmatic media buying engine in digital advertising.

In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)

Professor Rose Luckin
UCL Knowledge Lab, UCL Institute of Education

Tuesday 16th May 2017

What makes a good teacher and a successful lesson? Artificial Intelligence (AI) in the classroom is the key that is unlocking the answer, according to Rose Luckin, Professor of Learning with Digital Technologies.

Intelligent software holds opportunities for individualised teaching that understands when and how lessons veer off topic, at what point pupils lose interest and when they are at their most engaged and receptive – providing smart interventions that help teachers find the best learning strategies for their classes.

In this UCL Lunch Hour Lecture, Professor Luckin will discuss what AI is telling us about how we learn and predict how machines and humans will interact in the classrooms of the future.

Bring your lunch and your curiosity! UCL Lunch Hour Lectures, Tuesdays and Thursdays, Darwin Lecture Theatre, 1.15 – 1.55pm (term time)

Free to attend, live stream or watch online
More info: http://events.ucl.ac.uk/lhl
Join the conversation on Twitter at #UCLLHL

Max Tegmark Life 3.0 Being Human in the Age of Artificial Intelligence Part 02

Andy Chan is a Product Manager at Infinia ML, an artificial intelligence company that builds custom algorithms and software for Fortune 500 companies. He currently leads the design, development, and execution of the company’s AI strategies. Prior to Infinia ML, Andy was a Senior Product Manager at Avalara and helped the company go public in June 2018.

Andy graduated from St. Lawrence with Honors in Economics. After graduation, he moved to North Carolina and completed a M.S. in Analytics at Johns Hopkins University. He is currently located in Chapel Hill, NC and is pursuing his MBA at Duke University. Outside of work and school, he enjoys coaching youth soccer for NCFC and spending time with his two German Shepherds. Andy Chan is a Product Manager at Infinia ML, an artificial intelligence company that builds custom algorithms and software for Fortune 500 companies. He currently leads the design, development, and execution of the company’s AI strategies. Prior to Infinia ML, Andy was a Senior Product Manager at Avalara and helped the company go public in June 2018.

Andy graduated from St. Lawrence with Honors in Economics. After graduation, he moved to North Carolina and completed a M.S. in Analytics at Johns Hopkins University. He is currently located in Chapel Hill, NC and is pursuing his MBA at Duke University. Outside of work and school, he enjoys coaching youth soccer for NCFC and spending time with his two German Shepherds. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx

♚ Play turn style chess at http://bit.ly/chessworld
1 minute per move, 100 game match, match score: 28 wins, 72 draws, AI Landmark game, Stockfish crushed, Bishop pair worth more than knight and 4 pawns

Research paper: “Mastering Chess and Shogi by Self-Play with a
General Reinforcement Learning Algorithm” :

David Silver,1∗ Thomas Hubert,1∗
Julian Schrittwieser,1∗
Ioannis Antonoglou,1 Matthew Lai,1 Arthur Guez,1 Marc Lanctot,1
Laurent Sifre,1 Dharshan Kumaran,1 Thore Graepel,1
Timothy Lillicrap,1 Karen Simonyan,1 Demis Hassabis1


The game of chess is the most widely-studied domain in the history of artificial intelligence.
The strongest programs are based on a combination of sophisticated search techniques,
domain-specific adaptations, and handcrafted evaluation functions that have been
refined by human experts over several decades. In contrast, the AlphaGo Zero program
recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement
learning from games of self-play. In this paper, we generalise this approach into
a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in
many challenging domains. Starting from random play, and given no domain knowledge
except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in
the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a
world-champion program in each case ….

Read more at: https://arxiv.org/pdf/1712.01815.pdf

What is reinforcement learning?

“Reinforcement learning (RL) is an area of machine learning inspired by behaviourist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The problem, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, the field where reinforcement learning methods are studied is called approximate dynamic programming. The problem has been studied in the theory of optimal control, though most studies are concerned with the existence of optimal solutions and their characterization, and not with the learning or approximation aspects. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.

In machine learning, the environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.[1] The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible.

Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Instead the focus is on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).[2] The exploration vs. exploitation trade-off in reinforcement learning has been most thoroughly studied through the multi-armed bandit problem and in finite MDPs.”

What is this company called Deepmind ?

DeepMind Technologies Limited is a British artificial intelligence company founded in September 2010.

Acquired by Google in 2014, the company has created a neural network that learns how to play video games in a fashion similar to that of humans,[4] as well as a Neural Turing machine,[5] or a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that mimics the short-term memory of the human brain.[6][7]

The company made headlines in 2016 in nature after its AlphaGo program beat a human professional Go player for the first time in October 2015.[8] and again when AlphaGo beat Lee Sedol the world champion in a five-game tournament, which was the subject of a documentary film.

►Kingscrusher chess resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Kingscrusher’s “Crushing the King” video course with GM Igor Smirnov: http://chess-teacher.com/affiliates/idevaffiliate.php?id=1933&url=2396
►FREE online turn-style chess at http://www.chessworld.net/chessclubs/asplogin.asp?from=1053
►Kingscrusher resources: http://www.chessworld.net/chessclubs/learn_coaching_chessable.asp
►Playlists: http://goo.gl/FxpqEH
►Play and follow broadcasts at Chess24: https://chess24.com/premium?ref=kingscrusher
♞ Challenge KC and others for turn style chess at http://bit.ly/33uVY8O

Artificial Intelligence Robots Development Until 2019 – Machine Learning Robot Ep. 06
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1. Ibuki

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3. Pepper

4. Romeo

5. Sophia

6. Meltant

#Artificialintelligence #Humanoidrobot #MachineLearning

Developments in artificial intelligence (AI) are leading to fundamental changes in the way we live. Algorithms can already detect Parkinson’s disease and cancer, and control both cars and aircraft. How will AI change our society in the future?

This documentary journeys to the hot spots of AI research in Europe, the USA and China, and looks at the revolutionary developments which are currently taking place. The rapid growth of AI offers many opportunities, but also many dangers. AI can be used to create sound and video recordings which will make it more and more difficult to distinguish between fact and fiction. It will make the world of work more efficient and many professions superfluous. Algorithms can decide whether to grant loans, who is an insurance risk, and how good employees are. But there is a huge problem: humans can no longer comprehend how algorithms arrive at their decisions. And another big problem is AI’s capacity for widespread surveillance. The Chinese city of Rongcheng is already using an AI-supported ‘social credit system’ to monitor and assess its citizens. Does AI pose a danger to our personal freedoms or democracy? Which decisions can we leave to the algorithms – and which do we want to? And what are AI’s social implications?

A documentary by Tilman Wolff und Ranga Yogeshwar


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In the last week of December, 2028, humanity forgot about more than a century of pop culture. You’ve probably never thought about it, and never found it strange — but the reason is an artificial intelligence called Earworm.

⏩ is a series of YouTube videos from a future.


ANIMATED BY: Jordan Husmann http://jordanhusmann.com

I’m at http://tomscott.com
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The introduction of AI or Artificial Intelligence is a step that is creating waves in technology. One particular area that is going to be tremendously influenced by the introduction of Al is the field of education. It is time that educational institutions take advantage of technological advancements and modify their environments accordingly. With the current speed that everything has been updating and adapting, it won’t be long before we see Al playing a huge role in teaching. Here’s how AI is going to shape education in the near future.

Read Here: https://hitberry.com/should-students-expect-undertake-ai-designed-classes-2019

(0:50) Classes Beyond the Classroom
(1:30) Personalized Learning
(2:08) Smart Campuses
(2:49) Feedback and Monitoring
(3:29) Information Portal
(4:51) Final Thoughts

Please Watch:
9 Reasons Why Tesla Model 3 Should Be Your Next Car | https://youtu.be/p1CpYKKQ2kE

10 Best Supercars Coming in 2019 | Porsche, BMW, Mercedes and more… | https://youtu.be/Ir0IJiaiadQ

5 Skills That Your Child Needs For The Jobs Of The Future | https://youtu.be/ost7ESfcLZ0

Looking for Holiday Gifts? Here are Top 10 Bestseller Books of 2018 | https://youtu.be/cjrJ4z4w59A

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Transhumanism, Artificial Intelligence, Microchipping, and the Future of Education. The world is changing and the education system is going to go through some serious changes in the coming years (not decades, years!).

The goal is to connect us to the cloud, is this a good thing? We will know everything, the argument goes, but is that a good thing?

There are inherent dangers that people like Elon Musk is talking about and others. Where does this all go? Well, we pretty much know because they are telling us!

Here’s the links to articles and videos I discuss in this video:

The End of School

Google inside your head

Elon Musk Video

Please stay up on this stuff people, especially if you’re in the education industries and systems.

Good luck to you!

Mike Palumbo

Please like, share, and subscribe!

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Interview with Sneha Reddy | Google Hire for Prestigious Project Artificial Intelligence

Dr. Andrei Borshchev, CEO at The AnyLogic Company, presents at the GE EDGE & Controls Symposium 2019. Topics with video time links below:

– 4:46 What is simulation modeling? What is AnyLogic?
– 8:02 Digital Twins. 10:37 Case study: Gas Turbine Fleet
– 14:08 Why AI and Simulation? Some AI terminology. How can simulation help AI?
– 21:21 Example of Deep Reinforcement Learning using simulation: Traffic Light Control (detailed)
– 29:53 Case study: AI trained by simulation model in Ferromagnetic Core Production
– 32:26 Conclusion. What are the challenges?

AI white paper – https://www.anylogic.com/resources/white-papers/artificial-intelligence-and-simulation-in-business/
Find out more about simulation modeling – https://www.anylogic.com/

#AnyLogic #Simulation #AI #DynamicSimulation #DigitalTwin


Two useful disciplines are cyber threat intelligence and active cyber defense. However, there is confusion around both of these areas that leads to a perception of hype and cost instead of vital tools for defenders to use. In the case of threat intelligence, many security companies have offered a range of threat intelligence products and feeds but there is confusion in the community as a whole as to how to maximize the value out threat intelligence. With active defense, there has been an attempt to brand this strategy as a hack-back or otherwise offense based practice whereas the strategy for an active defense has existed long before the word ‘cyber’ and is focused around practices such as incident response. This presentation will examine the current state of cyber threat intelligence and active cyber defense as well as provide strategies for leveraging proven cyber intelligence models within active cyber defense operations

Robert M. Lee (@robertmlee), Author & Instructor, SANS Institute
Robert M. Lee is a SANS Certified Instructor and the course author of SANS ICS515: Active Defense and Incident Response and the co-author of SANS FOR578: Cyber Threat Intelligence. Robert is also CEO of Dragos Security, a non-resident National Cyber Security Fellow at New America focusing on policy issues relating to the cybersecurity of critical infrastructure, and a PhD candidate at Kings College London. For his research and focus areas, he was named one of Passcode’s Influencers and awarded EnergySec’s 2015 Cyber Security Professional of the Year. Robert obtained his start in cybersecurity in the U.S. Air Force where he served as a cyber warfare operations officer. He has performed defense, intelligence, and attack missions in various government organizations including the establishment of a first-ofits-kind ICS/SCADA cyber threat intelligence and intrusion analysis mission.

Erick Mandt, Analyst, Air Force Office of Special Investigations (AFOSI)
Erick Mandt is a 25-year intelligence professional with broad experience in cyber counterintelligence, signals intelligence, intelligence analysis, and language analysis. He currently works as an analyst for the Air Force Office of Special Investigations (AFOSI) open-source intelligence team where he supports a full range of law enforcement and counterintelligence investigations and operations. Erick’s research and analytical interests focus on integrating critical thinking and structured analysis processes into active cyber defense operations. Prior to joining AFOSI, Erick served 20 years as a cryptologic linguist for the U.S. Navy. He is proficient in Russian, Bulgarian, Serbian-Croatian, and Macedonian. Erick has an undergraduate degree in Russian Area Studies from Excelsior College and an MS in Cybersecurity from Utica College.

DFIR Summit Agenda:https://www.sans.org/event-downloads/43207/agenda.pdf
DFIR Summit Brochure:https://www.sans.org/event-downloads/43207/brochure.pdf .

While everyone seems to be talking about artificial intelligence these days, it’s good to remember that this is not something new!

Artificial intelligence today is properly known as narrow AI (or weak AI), in that it is designed to perform a narrow task (e.g. only facial recognition or only internet searches or only driving a car). However, the long-term goal of many researchers is to create general AI (AGI or strong AI). While narrow AI may outperform humans at whatever its specific task is, like playing chess or solving equations, AGI would outperform humans at nearly every cognitive task.

But after so many advantages do we have any danger with AI and the Future era of ROBOTS..?
I will explain the Science of Artificial Intelligence.

Sophia is one of the most Advanced AI humanoid robots.

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On October 3-4, 2009, the Singularity Summit 09 is held in New York, http://www.singularitysummit.com/ . Itamar Arel is speaking about Artificial General Intelligence development. How it is going to be designed, how is it going to be possible to manage its complexity, what is the roadmap for its development, and what resources are going to be needed. http://mil.engr.utk.edu/nmil/member/2


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Sure, some expert-level knowledge is needed if you want to program artificial intelligence. But AI expert Ben Goertzel posits that you also need something that Guns N’ Roses sang about: a lil’ patience. If you want instant gratification, this isn’t the line of work for you. Ben’s most recent book is AGI Revolution: An Inside View of the Rise of Artificial General Intelligence.



Ben Goertzel is CEO and chief scientist at SingularityNET, a project dedicated to creating benevolent decentralized artificial general intelligence. He is also chief scientist of financial prediction firm Aidyia Holdings and robotics firm Hanson Robotics; Chairman of AI software company Novamente LLC; Chairman of the Artificial General Intelligence Society and the OpenCog Foundation.His latest book is AGI Revolution: An Inside View of the Rise of Artificial General Intelligence.



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Ben Goertzel: My cousin who lives in Hong Kong is a game programmer, and he loves what I’m doing but he just tells me when we discuss it, “I need immediate gratification.” And he codes something and he sees a game character do something cool, right? And if you need that, if you really need to see something cool happen every day AGI is not for you. In AGI you may work six months and nothing interesting happens. And then something really interesting happens. So I think if someone doesn’t have that kind of stubborn, pigheaded persistence I will tend to employ them doing, for example, data analysis, because that gives immediate gratification. You get a data set from a customer, you run a machine learning algorithm on it and you get a result which is interesting. The customer is happy. Then you go on to the next data set.

And if you explain the different types of work available actually most people are pretty good at choosing what won’t drive them crazy. So some people are like “Yeah, I want to do stuff that seems cool every day.” And other people are like “Well, I really want to understand how thinking works. I want to understand how cognition and vision work together, and that’s much more interesting to me than applying an existing vision algorithm to solve someone’s problem.”

So I tend to throw the issue at the potential employee or volunteer themselves, and sometimes that works, sometimes it doesn’t. But I trust them to know themselves better than I know them anyway.

There are many different types and levels of problems that one encounters in doing AI work, and there are sort of low-level algorithmic problems or software design problems which are solved via clever tricks. And then there are deeper problems, like how do you architect a perception system? How should perception and cognition work together in an AI system? If a system knows one language how do you leverage that knowledge to help it learn another language? I find personally with these deeper problems this is the kind of thing you solve while you were like walking the dog in the forest or taking a shower or driving down the highway or something.

And it seems to be that the people who make headway on these deeper problems have the personality type that carries the problem in their head all the time. Like you’ll think about this thing when you go to sleep, you’re still thinking about it when you woke up, and you just keep chewing on this issue a hundred times a day. It could be for days or weeks or years, or even decades. And then the solution pops up for you.

And not everyone has the inclination or personality to be obsessive at sort of keeping a problem like an egg in your mind, in your focus, until the solution hatches out. But that’s a particular cognitive style or habit or skill which I see in everyone I know who’s really making headway on the AGI problem.

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AI expert Ben Goertzel is no stranger to building out-of-this-world artificial intelligence, and he wants others to join him in this new and very exciting field. That’s why he co-founded iCog Labs in Ethiopia, and he’s training people not through textbooks but online courses offered by the likes of MIT, Coursera, and Udacity. That way, they can learn about the many different skill sets needed to build AI much faster than a traditional educational route. Ben’s latest book is AGI Revolution: An Inside View of the Rise of Artificial General Intelligence.



Ben Goertzel is CEO and chief scientist at SingularityNET, a project dedicated to creating benevolent decentralized artificial general intelligence. He is also chief scientist of financial prediction firm Aidyia Holdings and robotics firm Hanson Robotics; Chairman of AI software company Novamente LLC; Chairman of the Artificial General Intelligence Society and the OpenCog Foundation.His latest book is AGI Revolution: An Inside View of the Rise of Artificial General Intelligence.



Ben Goertzel: There’s aspects, yes. AGI has aspects of computer science, mathematics, engineering, philosophy of mind, linguistics, neuroscience. It’s quite cross-disciplinary, and the education system isn’t really that way. It’s more that way in the U.S. than anywhere else on the planet actually. That’s a strength the U.S. has. Here as an undergraduate, you can at least take courses in every department. And in many countries, that’s not true.

But even in the U.S., the education system is not nearly as cross-disciplinary as it should be for grappling with a problem like AGI or with say quantum computing or nanotechnology or a lot of other cutting edge things.

So what that means is if someone wants to really work in one of these cutting-edge topics that has the highest probability of transforming the world, if they want to work on these things in the core capacity, they have to take their own time to study a bunch of other fields that they didn’t learn in school. And that also takes time. You can’t do that by reading a blog post. I mean you’ve got to, you know, take out a neuroscience textbook and go through it step by step. And not everyone has the patience for that.

But again some people do, and I’d say Coursera, Udacity, and MIT, the many universities that have put their courseware online have been a huge, huge asset in this process because those help lead people through the process of learning information from all the different disciplines that they need to attack something like AGI. We found these online courses incredibly useful in what we’ve been doing in Ethiopia.

So in 2013, I co-founded with two others Ethiopia’s first AI and robotics development company. So we do some original R&D, some projects aimed at helping the African situation. Then a bunch of software and robotics outsourcing. The company is called iCog Labs based in Addis Ababa. And we have an internship program which we use for recruiting.

So we take dozens of undergrad students each year and what we do is we give them some hands-on lessons in OpenCog and various other AI tools. We also have each of them take like seven Coursera courses. And they go through them very quickly and they teach them neuroscience, computational linguistics, bioinformatics, machine learning, a bunch of topics that are not offered in the university there.

And this works much better than giving them a bunch of textbooks to read because it gives them a process and a community to enter into. It not only teaches them information but it weeds out people who don’t have the persistence to slog through stuff from a bunch of different disciplines and really stretch their brain in a deeper cross-disciplinary way.

So yeah, I’d say, as with everything else there’s pluses and minuses all tangled up, right? I mean the modern way of doing things in some ways eliminates people’s attention span because nobody has to think for themselves. They immediately look up the answer on the internet or download something instead of trying to solve a problem themselves.

On the other hand, there’s so much high-quality educational material out there together with supportive communities for people who do want to plunge in deeper and get a more foundational understanding.

But what we do in OpenCog is we’ve worked out a system where each of…

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What could advanced artificial intelligence mean for humanity?– Second Thought
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From the very earliest mechanical calculators to the phone you’re probably watching this video on, computing power has come a long way in a relatively short time. We’re beginning to see some very promising artificial intelligence experiments, and that has people wondering…what should we expect? What could advanced AI mean for humanity?

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從1956年第一次訂立人工智慧(Artificial Intelligence)這個名詞,到2016年圍棋對弈一戰成名的AlphaGo,「人工智慧到底會不會取代人類」一直是各方焦慮的質疑,而隨著機器學習與深度學習的發展,人工智慧快速精準的學習資料庫內的模型,不管是簡單的圖像辨識,或是複雜的醫學影像,都能夠做到比人類專家更精準的判讀。
身為一位人工智慧研究學者,許永真提出”AI is to empower people.” 人工智慧應是人類的助力,能夠縮短高重複性勞務時間並降低錯誤率,是協助人類解決複雜問題的一項技術。
Will machines with artificial intelligence replace humans? This question has been the topic of discussion ever since AlphaGo defeated one of the world’s best Go players in 2016. AI researcher Jane Hsu argues that machine intelligence is not something to be feared; instead, we should embrace life with artificial intelligence as it is designed to empower people. Here, she gives a clear, easy-to-understand view of how machines that process information on a very sophisticated level will benefit humans in the near future. 臺大資訊工程學系教授。曾擔任台灣人工智慧協會的理事長與臺大資訊系系主任,其研究與教學主要著重於智慧型多代理人系統、資料探勘分析、以及感知運算。

目前擔任 Intel-NTU 中心的主任,協助促進台大、Intel與台灣國家科學委員會間的國際研究合作;也正於台大資訊系開授人工智慧的相關課程,並在創新設計學院開授「智齡設計-老人科技福祉專題」,期望透過結合資訊科技與創新思考的方式,帶領學生發揮創新思考並有能力將其付諸實現。


Jane Hsu is currently a Professor of the Department of Computer Science and Information Engineering at National Taiwan University, where she served as the Department Chair from 2011 to 2014. As the Director of the NTU IoX Center, established in 2011 as the Intel-NTU Connected Context Computing Center, Prof. Hsu is leading the global research collaboration on Augmented Collective Beings and Internet of Things. With more than 30 years of experience in AI, her research interests include multi-agent planning/learning, crowd-sourcing, knowledge mining, commonsense computing, and context-aware smart IoT. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx

Alan Turing probably never collected stamps, but he was one of the pioneers of Artificial Intelligence. Dermot Turing tells the story of how Alan Turing recognised that machines could learn, and considers whether the debate about the direction of Artificial Intelligence has moved on in the 70 years since then. What is the Robot Fallacy and why are we still trapped by it? How should we be planning for a world where AI is central to our lives? And what has any of this got to do with Stamp-Collecting?

IT Days 2018, http://www.itdays.ro

Could Artificial Intelligence end humanity? We asked one of the world’s leading experts, professor Stuart Russell. Be prepared to be freaked out

Professor Stuart Russell’s Book ‘Human Compatible: AI and the Problem of Control’ is out now:

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In this video we ask a series of questions to help you think about the impact of artificial intelligence for the future of your business.
At Prime Consulting, as Business Growth Consultants, we challenge you to these questions, to make you think about what aspects of our society and of the way we do business will change and how we need to prepare.
Human knowledge will be surpassed by artificial intelligence and our role as business leaders is changing. Are you ready for the future? Or will you belong to the past. Can you win this future challanges with automation ?
Find out more topics on our website – https://primeconsult.it/en/

Make your Google and Facebook Ads go further using Adext AI artificial intelligence software that targets the best performing audiences for you.



Einstein Discovery uses the power of AI to empower Business Users to understand and improve their most valuable KPI. Learn from how thousands of users just like you have already become Citizen Data Scientists and learn more about the Einstein Discovery Product and Roadmap.

This is the new model of agile operations empowered by the latest AI technology to deliver positive revenue growth and increased profitability.

Speaker: Dr. Joshua Elliott, Program Manager, DARPA / Information Innovation Office

The first era of human-computer symbiosis was ushered in by such early DARPA visionaries as Doug Engelbart and JCR Licklider, who posited that in the 15 or 20 or 500 years before computers could replace humans on all intellectual endeavors, symbiosis would make these “intellectually the most creative and exciting in the history of mankind.” Today, DARPA is advancing several research efforts to augment intelligence through symbiosis with artificial intelligence. This talk will explore current endeavors to synthesize, curate, and apply scientific and expert knowledge to some of the most complex problems of our time.


With over 400 testing laboratories around the world, Bureau Veritas is a global leader in testing, inspection and certification. From hydrocarbons to mineral ores, consumer products and food processing – Bureau Veritas experts control, analyze and test all types of components on the market. Today, Artificial Intelligence is gradually being installed in Bureau Veritas laboratories. New testing methods and productivity gains are heralding the era of “test 4.0”.

In this video I will explain what the main differences are between the current approaches to artificial intelligence and human intelligence.

For this I first explain how neural networks work and in which sense they mimic the human brain.

I then go through the ten most relevant differences that are: Form and function, size, connectivity, power consumption, architecture, activation potential, speed, learning technique, structure, and precision.

Finally I express my opinion that the benefit of research in artificial intelligence is not the reproduce human-like intelligence, but instead to produce types of intelligence unlike our own that complement our own abilities.