What happens when we teach a computer how to learn? Technologist Jeremy Howard shares some surprising new developments in the fast-moving field of deep learning, a technique that can give computers the ability to learn Chinese, or to recognize objects in photos, or to help think through a medical diagnosis. (One deep learning tool, after watching hours of YouTube, taught itself the concept of “cats.”) Get caught up on a field that will change the way the computers around you behave … sooner than you probably think. (More)

Understanding how society changes itself as it develops intelligence-extending technologies is key to recognising appropriate measures for both developing and regulating AI. In this talk Professor Joanna J Bryson roots “AI ethics” in the history and even prehistory of intelligent artefacts like language and writing, then describes the role of intelligence and communication in cooperation and competition. From here she looks at the political economy of distance-reducing technologies more generally, and information communication technologies, including AI in particular. Finally she makes concrete recommendations about what this implies about how we should incorporate AI technologies into our discourse, households, and laws. For more information about Joanna and her talk, and a podcast interview, see: https://www.anthtechconf.co.uk/speaker/joanna-bryson (More)

Because of the Youtube Live Streaming platform outage on Wednesday, this speaker was interrupted during the streaming session. The missing portion appears in this video. (More)

Manish speaks on the delicate balance needed for the relationship between Man Vs AI Mr. Manish Goyal: Head, Strategy and Sales, Xtage Labs, New Delhi. He is a certified digital transformation consultant and a trained AI expert from MIT, Boston with 15 years of experience in leading and developing businesses and advising enterprises. As Head of Strategy & Sales for an AI/ML solutions company Xtage Labs, he is responsible to drive business across borders and set the direction of company’s growth globally. 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 (More)

Joscha Bach on GPT-3, achieving AGI, machine understanding and lots more
02:40 What’s missing in AI atm? Unified coherent model of reality
04:14 AI systems like GPT-3 behave as if they understand – what’s missing?
08:35 Symbol grounding – does GPT-3 have it?
09:35 GPT-3 for music generation, GPT-3 for image generation, GPT-3 for video generation
11:13 GPT-3 temperature parameter. Strange output?
13:09 GPT-3 a powerful tool for idea generation
14:05 GPT-3 as a tool for writing code. Will GPT-3 spawn a singularity?
16:32 Increasing GPT-3 input context may have a high impact
16:59 Identifying grammatical structure & language
19:46 What is the GPT-3 transformer network doing?
21:26 GPT-3 uses brute force, not zero-shot learning, humans do ZSL
22:15 Extending the GPT-3 token context space. Current Context = Working Memory. Humans with smaller current contexts integrate concepts over long time-spans
24:07 GPT-3 can’t write a good novel
25:09 GPT-3 needs to become sensitive to multi-modal sense data – video, audio, text etc
26:00 GPT-3 a universal chat-bot – conversations with God & Johann Wolfgang von Goethe
30:14 What does understanding mean? Does it have gradients (i.e. from primitive to high level)?
32:19 (correlation vs causation) What is causation? Does GPT-3 understand causation? Does GPT-3 do causation?
38:06 Deep-faking understanding
40:06 The metaphor of the Golem applied to civ
42:33 GPT-3 fine with a person in the loop. Big danger in a system which fakes understanding. Deep-faking intelligible explanations.
44:32 GPT-3 babbling at the level of non-experts
45:14 Our civilization lacks sentience – it can’t plan ahead
46:20 Would GTP-3 (a hopfield network) improve dramatically if it could consume 1 to 5 trillion parameters?
47:24 GPT3: scaling up a simple idea. Clever hacks to formulate the inputs
47:41 Google GShard with 600 billion input parameters – Amazon may be doing something similar – future experiments
49:12 Ideal grounding in machines
51:13 We live inside a story we generate about the world – no reason why GPT-3 can’t be extended to do this
52:56 Tracking the real world
54:51 MicroPsi
57:25 What is computationalism? What is it’s relationship to mathematics?
59:30 Stateless systems vs step by step Computation – Godel, Turing, the halting problem & the notion of truth
1:00:30 Truth independent from the process used to determine truth. Constraining truth that which can be computed on finite state machines
1:03:54 Infinities can’t describe a consistent reality without contradictions
1:06:04 Stevan Harnad’s understanding of computation
1:08:32 Causation / answering ‘why’ questions
1:11:12 Causation through brute forcing correlation
1:13:22 Deep learning vs shallow learning
1:14:56 Brute forcing current deep learning algorithms on a Matrioshka brain – would it wake up?
1:15:38 What is sentience? Could a plant be sentient? Are eco-systems sentient?
1:19:56 Software/OS as spirit – spiritualism vs superstition. Empirically informed spiritualism
1:23:53 Can we build AI that shares our purposes?
1:26:31 Is the cell the ultimate computronium? The purpose of control is to harness complexity
1:31:29 Intelligent design
1:33:09 Category learning & categorical perception: Models – parameters constrain each other
1:35:06 Surprise minimization & hidden states; abstraction & continuous features – predicting dynamics of parts that can be both controlled & not controlled, by changing the parts that can be controlled. Categories are a way of talking about hidden states.
1:37:29 ‘Category’ is a useful concept – gradients are often hard to compute – so compressing away gradients to focus on signals (categories) when needed
1:38:19 Scientific / decision tree thinking vs grounded common sense reasoning
1:40:00 Wisdom/common sense vs understanding. Common sense, tribal biases & group insanity. Self preservation, dunbar numbers
1:44:10 Is g factor & understanding two sides of the same coin? What is intelligence?
1:47:07 General intelligence as the result of control problems so general they require agents to become sentient
1:47:47 Solving the Turing test: asking the AI to explain intelligence. If response is an intelligible & testable implementation plan then it passes?
1:49:18 The term ‘general intelligence’ inherits it’s essence from behavioral psychology; a behaviorist black box approach to measuring capability
1:52:15 How we perceive color – natural synesthesia & induced synesthesia
1:56:37 The g factor vs understanding
1:59:24 Understanding as a mechanism to achieve goals
2:01:42 The end of science?
2:03:54 Exciting currently untestable theories/ideas (that may be testable by science once we develop the precise enough instruments). Can fundamental physics be solved by computational physics?
2:07:14 Quantum computing. Deeper substrates of the universe that runs more efficiently than the particle level of the universe?
2:10:05 The Fermi paradox
2:12:19 Existence, death and identity construction (More)

Full episode with Joscha Bach (Jun 2020): https://www.youtube.com/watch?v=P-2P3MSZrBM
Clips channel (Lex Clips): https://www.youtube.com/lexclips
Main channel (Lex Fridman): https://www.youtube.com/lexfridman
(more links below) (More)

Full episode with Joscha Bach (Jun 2020): https://www.youtube.com/watch?v=P-2P3MSZrBM
Clips channel (Lex Clips): https://www.youtube.com/lexclips
Main channel (Lex Fridman): https://www.youtube.com/lexfridman
(more links below) (More)

James Hendler is a recognized visionary, who, along with Tim Berners-Lee and Ora Lassila, created the Semantic Web. He continues to push the boundaries of thinking in computer science and artificial intelligence (AI) research with his latest book “Social Machines: The Coming Collision of Artificial Intelligence, Social Networking, and Humanity” that highlights the challenges and the possibilities of the interconnection of human and machine intelligence. Hendler’s talk will redefine the vision of how certain approaches in systems engineering can enable massive improvements in productivity for managers of information via sophisticated and increasingly intelligent algorithms, while at the same time leading to systems that perform in ways that are designed to work as naturalistically as possible for their human operators. (More)

Her research interests focus on artificial intelligence (AI), with an emphasis on preference modelling and reasoning, constraint processing, multi-agent systems and voting theory. Among other things, she wants to study how people make decisions and compromise in social contexts, based on their preferences. On our stage she will share with us some findings of her research and elaborate on the present and future of AI. (More)

What the History of Math Can Teach Us about the Future of AI
Whenever an impressive new technology comes along, people rush to imagine the havoc it could wreak on society, and they overreact. Today we see this happening with artificial intelligence (AI). I was …
Source: https://blogs.scientificamerican.com/observations/what-the-history-of-math-can-teach-us-about-the-future-of-ai/ (More)

What the History of Math Can Teach Us about the Future of AI
Whenever an impressive new technology comes along, people rush to imagine the havoc it could wreak on society, and they overreact. Today we see this happening with artificial intelligence (AI). I was …
Source: https://blogs.scientificamerican.com/observations/what-the-history-of-math-can-teach-us-about-the-future-of-ai/ (More)

Rod Brooks, Founder, Chairman and CTO, Rethink Robotics (More)

from – Brief Answers To Short Questions by Stephen Hawking. (More)

Artificial intelligence has been described as “the new electricity”, poised to revolutionize human life and benefit society as much or more than electricity did 100 years ago. AI has also been described as “our biggest existential threat”, a technology that could “spell the end of the human race”. Should we welcome intelligent machines or fear them? Or perhaps question whether they are actually intelligent at all? In this talk, AI researcher and award-winning author Melanie Mitchell describes the current state of artificial intelligence, highlighting the field’s recent stunning achievements as well as its surprising failures. Mitchell considers the ethical issues surrounding the increasing deployment of AI systems in all aspects of our society, and closely examines the prospects for imbuing computers with humanlike qualities. (More)

“Future Cities and A.I. in Partnership with Dubai Municipality” by Prof. Stuart Russel, Professor of Computer Science and Electrical Engineering, UC Berkeley. #WorldGovSummit (More)

Berkeley EECS Annual Research Symposium 2/9/17
Panel 2 – Long-Term Future of (Artificial) Intelligence
Provably Beneficial AI – Stuart Russell (0:00-24:25)
The Future of AI is Here – Benjamin Recht (25:34-36:16)
A Glimmer of Hope – Alyosha Efros (36:23-42:12)
panel discussion (42:15-1:19:45) (More)

Stuart Russell (University of California, Berkeley, USA):
I will briefly survey recent and expected developments in AI and their implications. Some are enormously positive, while others, such as the development of autonomous weapons and the replacement of humans in economic roles, may be negative. Beyond these, one must expect that AI capabilities will eventually exceed those of humans across a range of real-world-decision making scenarios. Should this be a cause for concern, as Elon Musk, Stephen Hawking, and others have suggested? And, if so, what can we do about it? While some in the mainstream AI community dismiss the issue, I will argue that the problem is real and that the technical aspects of it are solvable if we replace current definitions of AI with a version based on provable benefit to humans. (More)

EECS Colloquium
Wednesday, October 16, 2019
306 Soda Hall (HP Auditorium)
4-5p (More)

*유튜브 한글 자막 제공
*Korean captions available (More)

Some machine learning proponents claim you only have to provide data to get value. However, reality is a bit more complex. On the way to active analytics for business, we have to answer two big questions: What must happen to data before running machine learning algorithms, and how should machine learning output be used to generate actual business value? (More)

In machine learning, no algorithm works equally well for all problems, a phenomenon researchers sometimes refer to as the “no free lunch theorem.” At the 2016 World Science Festival, cognitive psychologist Gary Marcus discussed what this theorem means and why it indicates that there’s no such thing as an unbiased algorithm. (More)

Here is a quick an simple explanation of what machine learning is. To summarize, machines learn from lots and lots of data just as humans learn from past experiences. (More)

Links to full videos:
1. https://www.youtube.com/watch?v=rBw0eoZTY-g
2. https://www.youtube.com/watch?v=oYmKOgeoOz4 (More)

Max Tegmark, professor and AI expert, talks about how we can build artificial intelligence that empowers instead of overpowering us. (More)

The Beyond Center presents the 2018 Beyond Annual Lecture with Max Tegmark. (More)

Artificial intelligence is beginning to be usefully deployed in almost every industry from customer call centers and finance to drug research. Yet the field is also plagued by relentless hype, opaque jargon and esoteric technology making it difficult for outsiders identify the most interesting companies. (More)

Launching an artificial intelligence startup may be all the rage these days, but it’s not as easy as you might think. Should you pursue venture capital or bootstrap your operation by focusing on profitability from day one? I’ll discuss both approaches in this video and share my experiences as a consultant. (More)

This video will discuss on the topic *How to Make Money in 2020 with Artificial Intelligence and Machine Learning* (More)

NVIDIA Inception nurtures cutting-edge AI startups who are revolutionizing industries through go-to-market support, expertise, and technology. (More)

DJI ne tank banaya hai! (More)

Ocado’s new warehouse has thousands of robots zooming around a grid system to pack groceries. The thousands of robots can process 65,000 orders every week. They communicate on a 4G network to avoid bumping into each other. Is this the future of retail? (More)

Wanna see more with Nao the robot? (More)

Have you ever imagined a robotic pet? The new Sony Aibo may just be what you’ve been waiting for. The new Sony Aibo will begin to ship in September (United States) and retail for $2899.
_________________________________________ (More)

Ever since Morse opened developer Tania Finlayson’s world, she’s been working to make it accessible for everyone. (More)

🔥 Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training
This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning Tutorial is ideal for both beginners as well as professionals who want to master Machine Learning Algorithms. Below are the topics covered in this Machine Learning Tutorial for Beginners video:
00:00 Introduction
2:47 What is Machine Learning?
4:08 AI vs ML vs Deep Learning
5:43 How does Machine Learning works?
6:18 Types of Machine Learning
6:43 Supervised Learning
8:38 Supervised Learning Examples
11:49 Unsupervised Learning
13:54 Unsupervised Learning Examples
16:09 Reinforcement Learning
18:39 Reinforcement Learning Examples
19:34 AI vs Machine Learning vs Deep Learning
22:09 Examples of AI
23:39 Examples of Machine Learning
25:04 What is Deep Learning?
25:54 Example of Deep Learning
27:29 Machine Learning vs Deep Learning
33:49 Jupyter Notebook Tutorial
34:49 Installation
50:24 Machine Learning Tutorial
51:04 Classification Algorithm
51:39 Anomaly Detection Algorithm
52:14 Clustering Algorithm
53:34 Regression Algorithm
54:14 Demo: Iris Dataset
1:12:11 Stats & Probability for Machine Learning
1:16:16 Categories of Data
1:16:36 Qualitative Data
1:17:51 Quantitative Data
1:20:55 What is Statistics?
1:23:25 Statistics Terminologies
1:24:30 Sampling Techniques
1:27:15 Random Sampling
1:28:05 Systematic Sampling
1:28:35 Stratified Sampling
1:29:35 Types of Statistics
1:32:21 Descriptive Statistics
1:37:36 Measures of Spread
1:44:01 Information Gain & Entropy
1:56:08 Confusion Matrix
2:00:53 Probability
2:03:19 Probability Terminologies
2:04:55 Types of Events
2:05:35 Probability of Distribution
2:10:45 Types of Probability
2:11:10 Marginal Probability
2:11:40 Joint Probability
2:12:35 Conditional Probability
2:13:30 Use-Case
2:17:25 Bayes Theorem
2:23:40 Inferential Statistics
2:24:00 Point Estimation
2:26:50 Interval Estimate
2:30:10 Margin of Error
2:34:20 Hypothesis Testing
2:41:25 Supervised Learning Algorithms
2:42:40 Regression
2:44:05 Linear vs Logistic Regression
2:49:55 Understanding Linear Regression Algorithm
3:11:10 Logistic Regression Curve
3:18:34 Titanic Data Analysis
3:58:39 Decision Tree
3:58:59 what is Classification?
4:01:24 Types of Classification
4:08:35 Decision Tree
4:14:20 Decision Tree Terminologies
4:18:05 Entropy
4:44:05 Credit Risk Detection Use-case
4:51:45 Random Forest
5:00:40 Random Forest Use-Cases
5:04:29 Random Forest Algorithm
5:16:44 KNN Algorithm
5:20:09 KNN Algorithm Working
5:27:24 KNN Demo
5:35:05 Naive Bayes
5:40:55 Naive Bayes Working
5:44:25Industrial Use of Naive Bayes
5:50:25 Types of Naive Bayes
5:51:25 Steps involved in Naive Bayes
5:52:05 PIMA Diabetic Test Use Case
6:04:55 Support Vector Machine
6:10:20 Non-Linear SVM
6:12:05 SVM Use-case
6:13:30 k Means Clustering & Association Rule Mining
6:16:33 Types of Clustering
6:17:34 K-Means Clustering
6:17:59 K-Means Working
6:21:54 Pros & Cons of K-Means Clustering
6:23:44 K-Means Demo
6:28:44 Hierarchical Clustering
6:31:14 Association Rule Mining
6:34:04 Apriori Algorithm
6:39:19 Apriori Algorithm Demo
6:43:29 Reinforcement Learning
6:46:39 Reinforcement Learning: Counter-Strike Example
6:53:59 Markov’s Decision Process
6:58:04 Q-Learning
7:02:39 The Bellman Equation
7:12:14 Transitioning to Q-Learning
7:17:29 Implementing Q-Learning
7:23:33 Machine Learning Projects
7:38:53 Who is a ML Engineer?
7:39:28 ML Engineer Job Trends
7:40:43 ML Engineer Salary Trends
7:42:33 ML Engineer Skills
7:44:08 ML Engineer Job Description
7:45:53 ML Engineer Resume
7:54:48 Machine Learning Interview Questions (More)