Analyzing the challenges posed by Artificial Intelligence at the 4th Heidelberg Laureate Forum – Experts discuss the costs and benefits created by developments brought on by Artificial Intelligence.

In recent years, Artificial Intelligence (AI) has established itself at the forefront of technological innovation. That is precisely why AI was the focus of the Hot Topic at the 4th Heidelberg Laureate Forum (HLF). The session was comprised of a panel discussion with leading researchers debating the current scientific trends in AI and its applications.
Today, AI is no longer a brash, cryptic concept taken directly from the pages of science fiction. The developments owed to the technology based on AI have altered what we thought possible and has done so in a much quicker fashion than was predicted. The power behind these advancements is already very evident with self-driving cars or algorithms that profile our Internet activity in order to tailor ads and search results. Though fascinating, this progress comes with a price of control and privacy loss that incites ethical questions.

The Hot Topic session took place on September 20, 2016.


James A. Hendler is Professor of Computer, Web and Cognitive Sciences at the Rensselaer Polytechnic Institute in the state of New York. He will give an introduction into current AI technologies.

The opinions expressed in this video do not necessarily reflect the views of the Heidelberg Laureate Forum Foundation or any other person or associated institution involved in the making and distribution of the video.


The Heidelberg Laureate Forum Foundation (HLFF) annually organizes the Heidelberg Laureate Forum (HLF), which is a networking event for mathematicians and computer scientists from all over the world. The 4th Heidelberg Laureate Forum took place from September 18 to 23, 2016. The HLFF was established and is funded by the German foundation the Klaus Tschira Stiftung (KTS), which promotes natural sciences, mathematics and computer science. The Scientific Partners of the HLFF are the Heidelberg Institute for Theoretical Studies (HITS) and Heidelberg University. The HLF is strongly supported by the award-granting institutions, the Association for Computing Machinery (ACM: ACM A.M. Turing Award, ACM Prize in Computing as of 2017), the International Mathematical Union (IMU: Fields Medal, Nevanlinna Prize), and the Norwegian Academy of Science and Letters (DNVA: Abel Prize).

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On the evening of 29th November 2018, the Leverhulme Centre for the Future of Intelligence (CFI) will host an expert panel on ‘The Future of Artificial Intelligence: Views from History’ featuring:

Prof. Simon Schaffer (University of Cambridge)
Prof. Murray Shanahan (DeepMind, Imperial)
Prof. Margaret A. Boden OBE ScD FBA, (University of Sussex)
Prof. Nathan Ensmenger (Indiana University)
Pamela McCorduck (author of Machines Who Think, an authoritative history of AI)

and chaired by Dr Sarah Dillon (Leverhulme Centre for the Future of Intelligence).

Speakers will interrogate the past, present, and future of intelligent systems for a general audience, with an interest towards the nuanced power dynamics that have operated around such systems throughout the ages.

This event will commemorates the 60th anniversary of the landmark 1958 ‘Mechanisation of Thought Conference’ held in Teddington, England; an event that served to establish artificial intelligence as a standalone field in the UK.

This public lecture will lead into a private symposium on 30th November 2018 involving world class historians of technology from around the globe.

Sponsored by PwC

World’s Top 50 Innovators 2019
Royal Society, 23-25 September
Artificial Intelligence and Robotics Session

Anima Anandkumar, Director of Machine Learning Research, NVIDIA. Bren Prof, Caltech
The path to embodied intelligence

#Top50Innovators #AI #NVIDIA #CodexTalks

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 General Intelligence)
• the values alignment problem
• Hume’s “Is-Ought” naturalistic fallacy as it applies to AI values vs. human values
• regulating AI
• Russell’s response to the arguments of AI apocalypse skeptics Kevin Kelly and Steven Pinker
• the Chinese social control AI system and what it could lead to
• autonomous vehicles, weapons, and other systems and how they can be hacked
• AI and the hacking of elections, and
• what keeps Stuart up at night.

Stuart Russell is a professor of Computer Science and holder of the Smith-Zadeh Chair in Engineering at the University of California, Berkeley. He has served as the Vice-Chair of the World Economic Forum’s Council on AI and Robotics and as an advisor to the United Nations on arms control. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science. He is the author (with Peter Norvig) of the definitive and universally acclaimed textbook on AI, Artificial Intelligence: A Modern Approach.

This dialogue was recorded on February 14, 2020 as part of the Science Salon Podcast series hosted by Michael Shermer and presented by The Skeptics Society, in California.

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Stuart Russell is a computer science and engineering professor at the University of California, Berkeley in Berkeley, California. Peter Stone is a professor of Computer Science at the University of Texas-Austin.

Recorded, 09/23/2016

Alexandra Suich, U.S. Technology Editor, the Economist

Guruduth Banavar, Vice President and Chief Science Officer, Cognitive Computing, IBM
Michael Ferro, Founder, Chairman and CEO, Merrick Ventures
Stuart Russell, Professor, Electrical Engineering and Computer Sciences, University of California, Berkeley; Vice Chair, World Economic Forum Council on AI and Robotics
David M. Siegel, Co-Chairman, Two Sigma

Shivon Zilis, Partner and Founding Member, Bloomberg Beta
It was fun to watch IBM’s Watson beat a human on “Jeopardy” in 2011, and we smiled when Apple’s Siri answered her first question. But as computers and machines become more powerful, faster and able to make decisions on their own, is this brave new world of artificial intelligence a triumph of human ingenuity or, as some fear, the beginning of the end of humankind? Some of the world’s most influential figures warn that artificial intelligence could advance to the point where we can no longer control it. They call for regulatory oversight, strict ethical guidelines and a ban on superintelligent computers. Others say such concerns are vastly overblown, that we are hundreds of years from having computers that are more intelligent than humans and that the benefits to society vastly outweigh the potential downsides. This session will explore the latest advances in AI and try to answer the question: beneficial or malevolent?

Panel Discussion: Artificial Intelligence Integration Across Subjects by Different Boards at 16th World Education Summit, Hyderabad

Key Discussion:
Digital Re-skilling for teachers
Empowering & Updating Educational Systems with AI
Integrating Education With AI and Machine Learning
AI will reduce time spent on teaching
Kavitha Jain, CEO, MDN Edify Education, Telangana
Harish Sanduja, Director Schools, Seth Anandram Jaipuria Group of Schools
Dr Manimekalai Mohan, Founder & Managing, Trustee SSVM Institutions,
Coimbatore & Mettupalayam
Manit Parikh, Country Manager- India, ELSA Corp
Prof Sanjay Gupta, Vice Chancellor, World University of Design

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Elets Technomedia, the premier technology and media research organisation of Asia and the Middle East, has spread its wings in India and across the world over the years Since 2003 it’s been championing the cause of the governments, building knowledge-sharing platforms and highlighting importance of ICT for governance, health, education, urban development, and banking and finance sectors through conferences, publications, and knowledge portals.

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#Elets #eGov #eHealth #DigitalLEARNING

Panel Discussion on Artificial Intelligence & Robotics by
Shri Shivesh Gaur
Empyrean Robotic Technologies

Billions of dollars are being invested in replicating the human brain. But will machine eventually totally replicate a human, including creativity? In this interview, we are asking Dr. Ahmed Elgammal, director of the Art & AI Laboratory, Rutgers University, and Founder & CEO, Artrendex about how AI, human creativity and art intelligence can coexist. How far AI potentially can go and how human art can benefit from it?

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Artificial Intelligence can be thought of in terms of optimization. Robert Miles explains using the evolution’s algorithm.

This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham:

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A new field of collective intelligence has emerged in recent years, supported by a wave of new digital technologies that make it possible for organizations and societies to think at large scale. But why do smart technologies not always automatically lead to smart results?
Chief Executive of Nesta Geoff Mulgan shows how this intelligence has to be carefully organized and orchestrated in order to fully harness and direct its powers.

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Nobel Laureate Michael Levitt, Cynthia Breazeal, Dzulkifli Abdul Razak, and Joel Mokyr explore the topic of artificial intelligence in a discussion moderated by Leila Janah at the Nobel Week Dialogue on December 9, 2015 in Gothenburg, Sweden.

Nobel Week Dialogue is a free of charge, full-day event and part of the official Nobel Week programme. The event aims to stimulate discussion at the highest level on a topical science-related theme by bringing together Nobel Laureates, the world’s leading scientists and experts, key opinion leaders, policy makers and the general public, online as well as on site. By bridging science and society, it’s an opportunity to stimulate thinking, excite imagination and inspire greatness!

May 1, 2017 – In less than a decade, the field of “artificial intelligence” or “AI” has been jolted by the extraordinary and unexpected success of a set of techniques now called “Deep Learning”. These methods (with some other related rapidly advancing technologies) already exceed average human performance in some kinds of image understanding; spoken word recognition and language translation; and indeed some tasks, like the game of Go, previously thought to require generalized human intelligence. AI may soon replace humans in driving cars, coding new software, robotic caregiving, and making healthcare decisions. The societal implications are enormous.

Suchi Saria, Assistant Professor of Computer Science, Johns Hopkins University presents “The Impact of Artificial Intelligence on Healthcare”

Artificial Intelligence and the Future with Demis Hassabis, Co-founder and CEO of DeepMind. How far can AI really take us? In this special event at the RSA, Demis Hassabis offers a unique insight from the frontiers of artificial intelligence research, and shares his latest thoughts on AI’s potential to help solve our biggest current and future challenges, from healthcare to climate change.

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#Ai expert Daniel Shapiro discusses #augmented #intelligence in #regulated #industries.
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Michio Kaku is a theoretical physicist, futurist, and professor at the City College of New York. He is the author of many fascinating books on the nature of our reality and the future of our civilization. This conversation is part of the Artificial Intelligence podcast.

Podcast website:
Full episodes playlist:
Clips playlist:

0:00 – Introduction
1:14 – Contact with Aliens in the 21st century
6:36 – Multiverse and Nirvana
9:46 – String Theory
11:07 – Einstein’s God
15:01 – Would aliens hurt us?
17:34 – What would aliens look like?
22:13 – Brain-machine interfaces
27:35 – Existential risk from AI
30:22 – Digital immortality
34:02 – Biological immortality
37:42 – Does mortality give meaning?
43:42 – String theory
47:16 – Universe as a computer and a simulation
53:16 – First human on Mars

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A Stationers’ Company Digital Media Group event at Stationers’ Hall on ‘Artificial Intelligence – Our Future: The Impact on the Content and Communication Industries’

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This Edureka video on “Top 10 Technologies in AI” will list out the top trending technologies that are going to take over the IT industries with the help of Artificial Intelligence. These technologies are most likely to start showing their dominance in the market from 2020 itself.


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About the Masters Program

Edureka’s Machine Learning Certification Training using Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. This Machine Learning using Python Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. Throughout the Data Science Certification Course, you’ll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR.


Why Go for this Course?

Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning


Who should go for this course?

Edureka’s Python Machine Learning Certification Course is a good fit for the below professionals:
Developers aspiring to be a ‘Machine Learning Engineer’
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Machine Learning (ML) Techniques
Information Architects who want to gain expertise in Predictive Analytics
‘Python’ professionals who want to design automatic predictive models

If you are looking for live online training, write back to us at or call us at US: + 18338555775 (Toll-Free) or India: +91 9606058406 for more information.

CeBIT Global Conferences – 23 March 2017: Keynote “Artificial Intelligence: Where are we at and where are we heading?” / Prof. Dr. Christian Bauckhage, Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS, Germany

This is an engaging discussion on current challenges and opportunities for incorporating artificial intelligence into mainstream serious gaming applications.

Moderator: Murray Newlands, Contributor, Forbes
Lukasz Kaiser, Senior Research Scientist, Google & Charge de Recherche, CNRS
Anil Earla, Head, Info & Data Analytics, Visa
Keenon Werling, Co-founder & CEO, Eloquent Labs
Lei Xu Senior Computer Vision Engineer, Tesla
Eros Marcello, UX Writer-Artificial Intelligence, Samsung


Kuhelee Chandel
Geospatial Media and Communications

Director General

Ed Parsons
Geospatial Technologist

President and CEO
PCI Geomatics

Ordnance Survey

Managing Director
South Asia NVIDIA

Dr Peter Woodgate
Chief Executive Officer
CRC for Spatial Information

#gis #IoT #DeepLearning

Artificial Intelligence Panel with Chris Boos, CEO of arago and Philipp Freise, Partner at KKR at the NOAH 2015 Conference in Berlin, Tempodrom on the 9th of June 2015.

NOAH Conference is Europe’s premier networking conference for the digital ecosystem where senior industry executives, world-class investors and rising startups gather to discuss market trends, form business partnerships and explore the latest innovations.

Home 2020 ND

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Facebook: AGI-08, the first conference on artificial general intelligence taking place in Memphis, has just concluded. Already plans for AGI-09 are underway. Check back on for videos of the conference sessions.

here is the original video published 4 years ago without the commentary:

Universal A.I. means the robot is able to not only play mortal kombat, but similiar games, like street fighter, virtual fighter, karate champ, or tekken.

The most important topic regarding modern Artificial intelligence is the robot’s ability to do recursive tasks. Opening a door or baking a cake or doing any human task requires recursive tasks. For example, when you open a door there are many recursive sub-tasks you must do, such as having the key, or turning the door knob. My AI program uses movie sequences called pathways to store life experiences. Therefore, recursive tasks are stored as linear possibilities. This way, I don’t have to pre-define rules or goals or procedures into the robot’s brain.

When I filed my patents and books starting from 2006, my goal was to design a robot with human level AI. The benchmark i used to test the robot’s cognitive skills and abilities is to let it play video games. If my robot can play every video game in the world, then it has achieved intelligence at a human-level.

In my first patent filed in 2006 (priority), I used the popular video game, Zelda to demonstrate my robots intelligence. I chose this game because it is very complex and requires at least a 6th grade level intelligence to beat. If the robot can play Zelda it can essentially play any video game.

The important thing is that I tried to demonstrate how my robot does recursive tasks. While the robot is managing multiple tasks, it’s also doing other things like:
1. navigate in an unknown environment
2. attack enemies
3. generate common sense knowledge.
4. solve problems.
5. do induction and deduction reasoning.
6. do multiple recursive tasks.
7. read and understand natural language.
8. identify objects and generate logical facts. etc.

The A.I. is playing this Mortal Kombat 2 game for the first time and is using a general pathway to play the game. Since this is the first time, the robot doesn’t understand the rules, goals, or procedures of the game. He uses common sense knowledge and logical reasoning to discover the objectives and rules of the game. In other words, the robot is using logical and reasoning to discover recursive tasks.

This video was made over 4 years ago and this is the first time I’m trying to explain how and why the robot makes decisions in the game. And the content in this video is based on 8 patents and 5 books filed from 2006-2007.

the data structure to Human-Level AI:


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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.

00:00 Intro
00:59 How Garry and Scott met
02:02 How Scott came up with the idea to work on AGI

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?

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

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 create a better world
29:14 How to be prepared for the long hard road

Full transcript at

Learn more about Vicarious:

I’m Garry Tan, venture capitalist and cofounder at Initialized Capital. We were earliest investors in billion dollar startups like Coinbase and Instacart, and we’re spending time with some of our best founders to learn the secrets of their success and see the future they’re building. I’m doing my own run-and-gun one man YouTube channel with no staff or crew — we’re going for raw and unfiltered, not perfect.

Please like this video and subscribe to my channel if you want to see more videos like this!

Microsoft (MSFT) on Tuesday announced that it has built one of the top five supercomputers in the world designed specifically for OpenAI. The company made the announcement during its Build developers conference, which is being held virtually rather than in person in Seattle where it is usually held each year.

The supercomputer, Microsoft says, will be used to train OpenAI’s own artificial intelligence models. OpenAI, which was founded in 2015 and received initial funding from the likes of Elon Musk and Sam Altman. Musk has since left the company, while Altman is now its CEO.

The company is working to create artificial general intelligence, or AI that is capable of outperforming humans, according to the group’s charter. The firm says its main concern is that such technologies benefit all of humanity, while ensuring its power doesn’t become concentrated in the hands of a few.

The new supercomputer has an incredible 285,000 CPU cores, 10,000 graphics processing units, and 400 gigabits per second of connectivity for each GPU server. While the computer ranks in the top five of the most powerful machines in the world, Microsoft won’t say exactly where it falls on that list.

#Microsoft #ArtificialIntelligence
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Sam Harris and Neil deGrasse Tyson talk about AI.

More videos on AI.

Link 1: Sam Harris and Philosopher David Chalmers talks about AI –

Link 2: Sam Harris talks about the dangers of Artificial Intelligence –

Link 3: Sam Harris talks AI at a TED talk 2016 –

Link 4: Sam Harris and Israeli Physicist David Deutsch talk about Artificial Intelligence –

Link 5: Sam Harris and American Cosmologist Max Tegmark talk about Artificial Intelligence –

Link 6: Sam Harris and Philosopher David Chalmers talks about AI (Artificial Intelligence) –

Link 7: Sam Harris & Paul Bloom discuss A.I., Dolores, Empathy in the Machine and Driverless Cars –

Artificial intelligence is already proving its worth among several industries including manufacturing and optics. It is recently beginning to chart its own path in the field of human resources and recruitment. HR professionals are fining ways in which AI can be put to good use to make recruiting, onboarding, and retaining employees more efficient and effective. This video will show some ways in which AI is changing the HR recruitment process.

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This Edureka video on “Future of AI” talks about the evolution of AI over the Years. It also lists out the different inventions of AI in recent times and how it is going to change the world in the Future. Following are the Topics Covered in this Artificial Intelligence Video:
1:40 Evolution of AI
4:11 Beginning of AI Revolution
6:57 Recent AI Inventions
9:48 Future of AI


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#edureka #edurekaDatascience #artificialIntelligence #FutureofAI

About the course:

Edureka’s Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on ‘R’ capabilities.

– – – – – – – – – – – – – –

Why Learn Data Science?

Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.

After the completion of the Data Science course, you should be able to:
1. Gain insight into the ‘Roles’ played by a Data Scientist
2. Analyze Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyze data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R

– – – – – – – – – – – – – –

Who should go for this course?

The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:

1. Developers aspiring to be a ‘Data Scientist’
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. ‘R’ professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies

If you are looking for live online training, write back to us at or call us at US: + 18338555775 (Toll-Free) or India: +91 9606058406 for more information.

AGI Rock Band in Beijing closing Artificial General Intelligence conference in 2013


For our English speaking followers, here is OCCAM presentation at GOOD AI: presentation of our theoretical framework
– Theory of incremental compression
– Relevance to AIXI approximation
Relating our work to the framework at GoodAI
– Relation to reuse of skills and gradual learning
– Cognitive abilities as special cases of compression
Description of our algorithm